{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tại sao tôi viết bài viết này? Cách đây hơn 1 năm khi tôi vào Đà Nẵng để chia sẻ ở sự kiện Cloud Next Extended 2018 về làm cách nào để có thể xây dựng một mạng CNN, trên đường đi từ khách sạn đến địa điểm tổ chức sự kiện, tôi nhận thấy trên taxi có một thiết bị luôn giúp tài xế nhận biện các biển báo trên đường.\n",
    "\n",
    "Tôi thấy thiết bị này khá hữu ích đặc biệt ở Việt Nam đôi khi biển báo nằm trốn sau cây hoặc cột điện. :D. Bài này chúng ta sẽ xây dựng một model để xử lý bài toán này. Ở đây tôi chưa nhắc đến việc làm realtime nhận diện biển báo, mà chỉ xây dựng mô hình deep learning nhận diện thôi. \n",
    "\n",
    "**Quan trọng:** Pretrained-model tôi đã train được đính kèm trong thư mục chung với file này. Bạn có thể tham khảo việc sử dụng ở phần 6.\n",
    "\n",
    "Đây là [link Google Colab](https://drive.google.com/open?id=1v4xCMmEZF8SAJNXLdPrTPBQH-gNqZ6oZ) tôi dùng để train Model: tỉ lệ chính xác của mô hình là 92,25 %. Tại sao lại cao như vậy? Rõ ràng một cách logic rằng bạn có thể nghĩ là khi thiết kế biển báo, người ta luôn cố gắng thiết kế sao cho dễ nhận biết nhất tức là đặc trưng khác nhau lớn giữa các biển báo. Vì vậy sẽ ít xảy ra hiện tượng nhầm lẫn.\n",
    "\n",
    "Tôi đặt tên mạng này là NgocNet vì là do tôi tự xây dựng lên, khi bạn code theo tôi bạn cũng có thể đặt tên mạng này theo tên riêng của bạn. Ví dụ bạn tên là Quân bạn có thể đặt tên mạng là QuanNet -> Quán Nét. :D\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Đây là ảnh một số đặc trưng trong [bộ dataset](http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset). Tôi có đính kèm link tải nhanh bộ dataset ở phía dưới."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"img/data.png\" width=500/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Thêm các thư viện cần thiết"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Như thường lệ, việc đầu tiên là thêm các thư viện cần thiết, Chúng ta sẽ sử dụng Tensorflow 2.0. Nếu bạn chưa cài thì có thể chạy script dưới đây."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip3 install tensorflow-gpu==2.0.0-rc0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import BatchNormalization\n",
    "from tensorflow.keras.layers import AveragePooling2D\n",
    "from tensorflow.keras.layers import MaxPooling2D\n",
    "from tensorflow.keras.layers import Conv2D\n",
    "from tensorflow.keras.layers import Activation\n",
    "from tensorflow.keras.layers import Dropout\n",
    "from tensorflow.keras.layers import Flatten\n",
    "from tensorflow.keras.layers import Input\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import concatenate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.optimizers import SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelBinarizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "import wget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "link = \"https://d17h27t6h515a5.cloudfront.net/topher/2017/February/5898cd6f_traffic-signs-data/traffic-signs-data.zip\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Tải dữ liệu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bar_custom(current, total, width=80):\n",
    "    print(\"Downloading: %d%% [%d / %d] bytes\" % (current / total * 100, current, total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "wget.download(link, bar=bar_custom) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Link Folder lưu dữ liệu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_folder_link = './signsdata'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tạo thư mực lưu data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isdir(data_folder_link):\n",
    "    print('new directry has been created')\n",
    "    os.system('mkdir {}'.format(data_folder_link))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unzip vào thư mục data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "replace ./signsdata/test.p? [y]es, [n]o, [A]ll, [N]one, [r]ename: ^C\n"
     ]
    }
   ],
   "source": [
    "!unzip -q traffic-signs-data.zip -d $data_folder_link"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_path = data_folder_link + \"/train.p\"\n",
    "test_data_path = data_folder_link + \"/test.p\"\n",
    "valid_data_path = data_folder_link + \"/valid.p\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sử dụng thư viện Pickle để đọc data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(train_data_path, mode=\"rb\") as f:\n",
    "    train = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(test_data_path, mode=\"rb\") as f:\n",
    "    test = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(valid_data_path, mode=\"rb\") as f:\n",
    "    valid = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data được lưu dưới dạng {\"features\":[], \"labels\": []}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bộ train được sử dụng cho quá trình huấn luyện"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X = train[\"features\"]\n",
    "train_Y = train[\"labels\"]\n",
    "\n",
    "\n",
    "# Tráo bộ dữ liệu\n",
    "train_X, train_Y = shuffle(train_X, train_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tại sao phải tráo bộ dữ liệu hiện tại, bởi vì các ảnh tương đồng nhau đang được liền nhau, cho nên để tăng tính ngẫu nhiên của dữ liệu ta phải tráo chúng lên."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bộ valid được sử dụng cho quá trình kiểm soát độ chính xác của mô hình khi training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_X = valid[\"features\"]\n",
    "valid_Y = valid[\"labels\"]\n",
    "\n",
    "# Tráo bộ dữ liệu\n",
    "\n",
    "valid_X, valid_Y = shuffle(valid_X, valid_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bộ test được sử dụng để kiểm tra độ chính xác của mô hình sau khi training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_X = test[\"features\"]\n",
    "test_Y = test[\"labels\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show 1 ảnh từ bộ test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x19b2b49d0>"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(test_X[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Danh sách các nhãn "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "classNames = ['Speed limit (20km/h)',\n",
    " 'Speed limit (30km/h)',\n",
    " 'Speed limit (50km/h)',\n",
    " 'Speed limit (60km/h)',\n",
    " 'Speed limit (70km/h)',\n",
    " 'Speed limit (80km/h)',\n",
    " 'End of speed limit (80km/h)',\n",
    " 'Speed limit (100km/h)',\n",
    " 'Speed limit (120km/h)',\n",
    " 'No passing',\n",
    " 'No passing for vehicles over 3.5 metric tons',\n",
    " 'Right-of-way at the next intersection',\n",
    " 'Priority road',\n",
    " 'Yield',\n",
    " 'Stop',\n",
    " 'No vehicles',\n",
    " 'Vehicles over 3.5 metric tons prohibited',\n",
    " 'No entry',\n",
    " 'General caution',\n",
    " 'Dangerous curve to the left',\n",
    " 'Dangerous curve to the right',\n",
    " 'Double curve',\n",
    " 'Bumpy road',\n",
    " 'Slippery road',\n",
    " 'Road narrows on the right',\n",
    " 'Road work',\n",
    " 'Traffic signals',\n",
    " 'Pedestrians',\n",
    " 'Children crossing',\n",
    " 'Bicycles crossing',\n",
    " 'Beware of ice/snow',\n",
    " 'Wild animals crossing',\n",
    " 'End of all speed and passing limits',\n",
    " 'Turn right ahead',\n",
    " 'Turn left ahead',\n",
    " 'Ahead only',\n",
    " 'Go straight or right',\n",
    " 'Go straight or left',\n",
    " 'Keep right',\n",
    " 'Keep left',\n",
    " 'Roundabout mandatory',\n",
    " 'End of no passing',\n",
    " 'End of no passing by vehicles over 3.5 metric tons']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ví dụ ảnh có nhãn là 1 thì tên của nhãn sẽ classNames[1]. là Cho nên nhãn của ảnh bên trên sẽ là:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Speed limit (30km/h)'"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classNames[test_Y[1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vậy nhãn của chúng ta đã tương ứng với ảnh, đây là biển số không vượt quá tốc độc 30km/h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hiển thị 25 ảnh ban đầu sau khi xáo trộn dữ liệu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UeDImWrPbP09p23NQyYvTnkXs/mp1qzHSV17wupLrGW/xFLtDc2B45wYAwA+/5S4pel7G/4Ejotx8hripBQzShaTvrUdl0PYCGisU5xM9YpynKMbquenF3D50QnKlbZwUtek778potZu2PJ0fO/n0V3L76RMyZ0cDGYdxR4ITbh/dmdtbhuQ+WhSAdDXR78+IgmQm3jNBwHMmLAh6CAAzzew8jzwlAS8f/MKf5/bsoQek8OI5sXmeFsegLQ/gyx8UC+QAzr8WS7+cWRCK82/PHgUAnHhGAnf+wHs+ktsb9kkOvCQs3qZjaDtD6HF8rmKqPlMoFAqFQqFYGfSlSKFQKBQKhQIrpM+iKMKGcafwIQplOQEbPfVVQVBDL0Ac5VvptMTdN98Wd1wYy/HFRXHNgSiziCizhIL8xUS5BIm4JxNSqLHyqEoU20BDXPeDjYyKCRuioDAUiJCVI6wI8pU3/H66djmVGLyrn9VXHFyMqRemzKpkR45aaURCUQ2Som+oRrnMyOVe4VxdFPQwIDrMpMUUR8AUB5WxdJ6ExkFA97ee1FODY+sAAOea4s6fX5SxlPQ4Nx8H0SM1FnVlhQM5pqW+5isKk+dXkmsHZXwou/HZ+0x25O6BRjTSLnEfzImU5Cv0eFdWwtE5eQZ4bctqtZjUpolQ5wH1YRTK+Kq6MdjtEW3O065EZekHkS3zwa/2b0oL49qB52BMN9Bj7oPKtCvS3puvuz63P/y+dwAAWkdFhfXXh2SsvzQta1hClJnlbRIlij1DY4lVjTHN8bAm9tZhUa7t2TKZ29smhc575Ywo3f70SwcAAHPfsy8/dveNcr6k8nxut0NRoFoj596yYWNuz009JLfRFVXUasFAchCmZuknGQoDGkNUawBwriVt/tWHHwYAPPr5T+THmi9KgEp0KABjkdobQEpz0xAtjkj6ISR6mroWKecq7QkFyVsOeP4ipa0v09l3n35MFHJdeoa//55/nNsb9knQR0s5L701leZCX7m2EmZbPUUKhUKhUCgUWGmcIgCm/3ZZEj+DEaS8kYw9EPzrI/uv522iFq/OwlnZJNY6L2Hbe+2Z3G7PySZBS16mCocLp5D2KUrifZB3oTUvb9pnKXN4hzxOY25j57pJOV9tUOLjGNrgS/vC/A1uXrZfTvmxBvEzXPuEngdBTI4F4aVu4E2WEW9Cz35hDFfkF/sA/VqsU7uH9CuCN2pWyK7WOFYUZWpnDxz1cUz916MN0wFt+EtoE3CcyPFGNeurSYqZxL+wZimFCMcU4T7zYzLJvXbWyFOUd6gXp6h4oyKPriTlMjJmI3c+y96hLnmHeJM9eUij2gDZ1G8cW4S/yx4hrw/lunFXvEO8kTul+Wgiue+68xRZK9fvUrwqHxf/XenH/1ndjfPGABW3eKXUN1Vaw2o1ae8eZL7t2bcptz9wj8T9Wfdy5vX8jYdlXTs+J2KSNBBPUUyPh1qF0nMMy3XGSZTyIq3RXeq/xhZJHXLb/qtz+2075Pi2UfFKTU1JvJuoJx75w89ndf7kfUfyY+33iNfog7dJ3Y++LLF61m/ands7dooH6etfvj+3a1VZA1YNRgiBgLwdvL4Ymg8JrS9NcuM+QhuTH7nvT7LPX3g4PxZ0KO4ae9B5s3Jd5mbYoGcVefTZN+w928mMeE1jhoXW3WRRntHoyfw1bgd+vCBj8fBhiS91/33Sn+8eWp/bY1slhQi/wfipt1Y+N9VTpFAoFAqFQgF9KVIoFAqFQqEAsFL6zBhx83nZa3nDGMVFKUlFnZDrrdvJ3PHtedn0d/60bLJrnhWXatASd1uvJ+WHKG7J+LC4Zi259Jkm48zCPaZTaDNtHMvmsW5H7HmKt5AiKx9Q5unxUG60Eol70gbinrTguEbFm5nXgj3rc09eOCmPSive3Mwbaau0QW/Axf1pED1apbbmyFY1+l5jSNyljWGJT1IfluMhpV0JSjbvxj1ptA5tkm4S9dXk2FU9cp279DAch2eMaCCmTTsdGSfsXY4pMEtCNMKahJwCkLrgIYbHTsQbkaXdejFtKGbaj0QFeYbsLt8vbc5sCPVRIzuqNqg8rxNlG3WLRRshxbJKe3LO1pysA7zx2/JmePe7r84Udiqfdyk1kB+bqLizeI1b7RhilSjApolsTTs9L+N+guJ8VSpCY7ZqMk9+/H0/ktuDqfT9bz10FABw7LzQSGkgbcr0/uC4rFVv2i9Z7N++e0Nux1NCh/zmF4X6mPJie0nb33ytpHG4YbNcd/6czMeHnhB66MCRl3J7eiGj+WhK4S8/JzGNku/flds/dZOk9li/SdaSc6eeze2DzwkNd3TqKNYCOa1TEvvKS8lC9qFXhJp86It/mdvNF1ybd+Q5yDHbAhr30fA6qUddKFNLm6i98U3tHJjihzhvejYkdggq0rdBTdaEeF5iRtlWRqsZ2l4T03aVZ576em5v3ynj5s51Py7nHpD74DQ3eSqSFcxR9RQpFAqFQqFQQF+KFAqFQqFQKACslD6zorwJyrL64uK0AWe1j5sZhTF/RuJnLJyWbMhpj3awU2yigbERsidyu0o76kOiP0wg7sSUgkXERJ81F8QV2WqK27LaZfpF6tPtZN+dozBJAdEPYxSrpjogtJ4XH8JzWxan/1gt9K9d1k+cYoDjpVTJXTpAtNaAo2eqJEyI6DYaA0JzDq8TVcHQuLj0GyPSfxHRBTbkIUvxT8h1yqqk3rC4bgeIhps7J/06f17GXLed0TAcZ6NGMV8GqlL3ZkQKiy7F6LBMHZNaL1wb/qwoClDixfPglBFiR8RLcPyvtK/kpFg1EcXkqg4JPeHFOQmLx3TKKjOqd+iJUUtieFHsq9qIzKUe0dkxpf5JnNLMi6lF60GcUobwEnVgWeoFs8pzc3RgAO993R0AgIMVudfO0ancfuWs2MMTQnE1E2mnL31FMpYffCWbp4nheETSHyMbhWK55937c/t1k0KHHD90NLfve1q2NZztEN9iZRwsnJO5ceqE0CPzY7R9YlHW1h+4S1KQbByV9eHzD70AAOjM0nPjvJzva397Jrfvvl7qvuEFSYHxwuMPShUpXtq1+2/FasNAYojxfGSlVI84qzMtGbMPf1PSdcw8+2X5bl99Td+zNAfDYXkmBgOypnrK67I0Nl7cQVozvPRbtMZQDDGeGbwVIRoW6jVxpdIWPdtpDVo4J3P6sW98Nbd3XyN9u/lqGSv8bLgcals9RQqFQqFQKBTQlyKFQqFQKBQKACtWn/lqkj4su5+9dBbsqqNgdm1RjMy4gF2tc+KOrVKwvR4pekbXy070+npxx6UD4hK25CLvBZyggMCx1sltV6Os6aZOLrmuUCestlpcyFy/3baoWljpVK2L6zmk4INRjekFVlJxEL21SPnhAsSxAo6VhF6ATrHrHJyR1E0Nd56I+qxBaVFGx8RFP0Jh9+tj4ipnyowDQ7LaIaB+DYy4bnukJouJVqvReYaJumRX6/z0tDuHjM2IaN5B+l6LAtfFpGiK2TXuCa3W5jeIdFExfRVTpQKax9znNrmQSgpq0odhlcZ/S+jmhMbK8KDQanFbKJRWmwIt0jUHBoXq5OCdbVJ9tlqUqoW+WyNaM6C6W/fdlBRpFUoDwsE1ux59VkwdrCWCoVFU73oXAOCfXSfU2KlvfCu3/78vCK1wtysLAOdekq0Hjx8Sqji22Xyz1E+DRJl94O1CTVw7ImU+/6Vv5vaDT0tgxLNNmkekMvMCwdK6+Pzh87n9gizjOHbwaG5H6yXY4q03X5PbzYWM6vybR4UmO0cxRM9OCQ3zF89IHe8dFWXb8WPSLueJ8m9YGWOrBWvpGUmpi/iZmJD9/El5Fr7yhLR/Oif3HzjaLOVUTEOypSSuEE1KAVdT2h4QU6oOVsvyVolBUpV26bk5OyNjq7cgfQvI/dXpubx+TFKuRI7aSynoo+lSWhKi406cfDm3X3pa0rNs3nWTlI84tUvWjprmQ6FQKBQKhWKF0JcihUKhUCgUClxG7rN8m3pQpHVZUpT1MBRYr7tIOY0WM7dZtyW0U0CUBIbFJdgelR3186Sy6MyIWz4IxfU2OiJufKayOkSbdDl3FrkZE6JOGoYyvQ8L1WNcOMKU8rp02mK3m+ISrHOwKaLpQPmMPO1ZAU15JWGMZEFPS3bsM33GFFSNKI4aUX79fGZVUh81SKFUIdVQUhfXapPpMGIyEspVRWwmBhrU91T+/CLlzFuUfqjQCB0kReDQKAUyc0EAF2c5wB8p0Yhu4YCAC9QuQcLjh13Wa5P7LO/Fknx6AWe85jxopDjzMls71VlI9FmH8wMSNZbSOGiwUm+RAi3SbzFDqpnFpoy/QcrNtEjKspDmYEIZt3tGyvMcT1x/GqIr0lTus0Lzrhd70Utzy5sVaxWBE8DgYANvuiPLDm7OCwW07RrJej/y+JPyBSP98PgBoTJmuhT80rV9tS7tePeNe3L79o3SZ9/8utAUX31cqJyZdnEwXBInSvA8wNumcPQV6cujJ4WSqU/LfPvaYQnI2HiL0Hm3unq+8ILQZI+ckWcI5818/tFjuT12r1AsmzZ/RcqfosC8p4VuWy1Y2DwnnGF/BDVcix55x5+Xdlg4+ZR8kJAy1P0fEBWYVKRdp8+JsrbXpIC1fA5vbsh8rFaljpUNkm9sbpHotgUJxjjc4Lx58t3+9hIAqNJ2hrH1WwH4udfSHtGYKW2JoIZ58UVplxsXjtP1aR2/jFcb9RQpFAqFQqFQQF+KFAqFQqFQKACskD4zkGBoltzlKRNohgM8yeG4Ry6xpqhWYkcxpaQ4awxSnqNRoVyOkAKlSUG/ArpQnWgnVp4MjQv1NkNB/k7OSBC09qzsqG+QR30dBfHbTAEI+zvzu7TjPqZcTN1Wq/B41ZJ0goKdcf4ZDo61OpA8diHTnERrMN0RBsX0Gev7Akc5NobIPT4myoAWKfDOzwu9Fc9TAES6JgfVGx4UKmBkRNylTP0tUPDNJgfyIyqrTX25jlUojqbtNKUuvQ7RSsQKVDj4JmV0Yxd0zLnP0jWgXgxy9WJECsk0oVxiIeWPozFrSXHHgQkD118R0VJ8L9WUFKU0f5ku5DWgMShudkPKk0Wap2lE44/ol0HKidfpyNznYHhBg9SeLiJkEnt8rJSt8BLIFPYy+mqVhaFpdwHN41n+p0994gv58fVjQilNk5LuyIxQXC9MUQBTSJslbsyOTMixW/eKgvcIBWb8u6dkHZzrkAKUxzrVN+C1KvR42dw8R/ksv/pVUVFto+45MS3z7f6nXsjtq783ow1vvFa2Lhw+L6qktCf1ap+S6z88I+P9qhuEKpw+/VhuT80J3baaSPp5CWnFNNSK51py79NH5d4sK7tYueb4b1OX/kxpTMS09aBK21EGaQtDSrlB52bkOlXDFBvlk2wKfTlck++OrhclcRxIm/es9HmbgiGnY9l6E9VkvnJdeNxYWrtPn5CcdXMzRJ9tuCG3+7lXVzJF1VOkUCgUCoVCAX0pUigUCoVCoQCw4uCNJg+o1+NgT1TEE2nQH3FXXHIefdbJaA7Of9QYFHokHBL6pTonu+jNgLgKmf6pkCu31xFa4NyUuO/Okbu+2ZHjtUAoAFbBhBRYq03frQ1k7r8oJmUM3WfSI1c2BazsdeQ6lUjoOJS03Wqhn9uMlW7WsnJJyjJlVKXyFc6h5b5aHRZVQY0CNqakSgqpHRuU18rSuRdpnCSk7GpS0EBupWoo7T22XuqwQK7pdpOUgnSv1YHMDV2piYu4RUntDOeBC5lWpNHPucWI1knsalOhACxg3PUToq+Y9owCynFGv4viEnovdDRcSP3j55qTdu32ODdYcQ4/am4vrZkpod89cDsbpntJ0Ub1DFyA0SQhhRytWRHlKORfiIk/CQuvudr8WbcV42VHYT340LP58XkrY3Pr7XfndjIl9zXfI2UrqZsSNw427ZbIieMD8r2Hjkpww1kOWkrnWLC0VgXc37K2+cQa0cy0feL4MVn/XuE6El16+JBQL8/uz6ivnXvlubDjcbnKAu1GmFuQc9x3Vuq1fkyo231bd+Z2fU5UWqsGa/Ngi9bS84GG1/l5WWtmKPcnYqa26ZRuPBoKpBtSW3KQ4SrR5gPjQkE2Y+0WRjcAACAASURBVHoNmJVna63Gc4oVh1RhVkfTw9twgFiiqOP2hXnTQlKAcm5DkBKNtyTM0JaLhWlpL87DahHn1qVCPUUKhUKhUCgU0JcihUKhUCgUCgCXEbyxr/axTJ+xZ5vKxqxCIcooJtu43eURudoqpCIZJCpmJwU9jMk7dm5O1Ci9RXEDphwEkpQn5KjDOClrhqtVKsO5zyjIFVEncHUOYw4+SK5EqiPTKZboFM6/xa7HcJXpM2stenE/oJhci9UGFQqqWAlYcUaKJhoHfZdtg3JfDRF9VqtTUD+iNlkt1SO1UJfoGc5VxUxKlyjKWlXafpiCftpIxluXFGUpuYCrtazv66SIWGQlFrVLwPdPgz8kitHPLYZVhzGSE4z7k8cU5zQq+1VkiNcK3P3wMaZH+DrsWmcK1hvGHFRyWXQUn7+YgjTeBcjt78arscWLU8Q5/lj1aZfTWas7N3vzTZz8yqMAgMWm1KdbFVVnFaIcay7IFoCoSuvlepkDZxazdXbTRsoTtSgURNqV9t1/vVBsxw6JKqm5IGuoTYspT5sGhcd90OPHMhVNY68l9338dDZnb7lGKLBtg7KWPCdLvqeCPEH81Lpr35Hbt22X+3jhuZeoXv+xpL6Xj/4wZaqY1b0LFICyuSj96Smx+FnRXw9pzeH2W7eOggyTmswS1dVqMk1FWzpqMoYQ0NYGyj/JOdQ4QCqve4YC27KStD92vJSQYbEKm+daTA/9eFHqy22UumeJ5j5TKBQKhUKhWCH0pUihUCgUCoUCK6TPrLWIHb3hecWLxSawpGJIYqayxN3VD7bEO9irRJ9xHqWJqhxvknrp9JQEYGxSvrFKTWicWk3OXyO/XUTBpgaqlOuL6KI2uRZTUN37Lswq0We9kqa1HpfGH+RWGR2xGrAWeV/ypbiaNeIZmW4goaCnygqcC7RClGRjQPpgmHLRjdNF26RMOTldrAap1aSfQqJRO13pb0Pqi5BoMI6VmTJtwuIH5+oNKbgj0yqcE4zzF3k0GQWuY2WFXRYlc/kwOX3GINUHu7m5F9ldXUDbFqlesg/IVb6MWywtYksWkGLBmX+isgv31T5lSjhPWUYmz4Vl1ffKo9Nt48jLzwEAYqId2A7I7lGgvsFxWX9uvPWa3O4uZlTJ+JisVSFkXbvz2t1yHVrPTr4kWxMs02de6xTPDU/JSqU93qREumwp6Oi5ZnZdQzn4GvQssKx+MzLGO0SttweExrc1UqXtXX2Vr4VB7NrC2xZBatCYtnokpDhDSRDf/lwPaA7y3DS0FcRaCupI50soT2dkZJHk9Tum56Ch7RS8NcWj9XibAQX5tdznbssFe2Zi7xlUomRlxpbbha5pLmOtVU+RQqFQKBQKBfSlSKFQKBQKhQLAZajP+q6twAv4x59z7qJiF5dvZ/8ZLxgU7Vr33t8oaBupHPxTFwdf8529rJSR4+zuizgQXEA77UlFFvbps+hCxQ6whJXwrllMn3EZSgm0KjCmOD8U577yaCI6HrEaibgp4+49JNdthfKLRRVxf6fEU8Q9CcbYJVo0oOsPEKXao2BkCeU78+izkPuPXL2mmJPpK6w8pZUt5hVZOeVRZqSQi1Kpy5rkPoOM66CkD735U8JYsZ3mAeeIIuWiHNCSj3MAN1M8H1FCa5XaPE9ZLUalOeea2Bw8kpRcTCOUqEF9aru45quBVprgqYWMFu6kQjn3SGXTiilfINX51KJQIptojXz7rVcDAMaHpexIJAFqNzVkHv35Fx/O7WNzQk9bS1SKV2MeFcWt49OVTGMWKxJ5Htb76wqV7bGaie6/UpP7mByiQLtUl4hooPWbtxTW98rCIuivk0wRcuBRvnfaSlI2S/I1pWSNsqwIpHP3SJEXkwJ4gLcNkGqMlbMVOm47Jc9zouUjw89CymnZn5s879KLP/EM0Y3UhUvG4soD5aqnSKFQKBQKhQL6UqRQKBQKhUIB4DLos74nzleDsGqgJBgh0WNs992G7M7ngHyGFGxNChTY6Yldb0hQr7TLSjFyG8YUBIuCBVZqYhuiQlq0M7/XFTfjcENomcjRbUmPA8GxGovVSMU2O0hTn4PAaqNPlTHDkZILlu2SGHy+27efS43LUhtwe3B/L1JAxQ7ZA0RHDQxK4LpFUpPZkvrCo3yYTmIKlt3x2f8xjTHOIcaBD40X7E8umfBNlbTpqsEib9S0TBlEypwUQi1wPFKmxFIXPDMhdZMJKf9VqWqtZHz3OPgfdWJJ9FdWnnBOQUtqRUOhWFOqZ9oP1srjk6jtlIOpegqf4n7mNi2NSXiFkNgA53vZfaXeOJL2WyTVZWNA1rBuS77wtQeezu15N5Z/5A7J+5W0hYK77/HncvsrL0jwwFa3WLHnKcs82ovqy5XnIKKlpBBD+qcxm9Xz+GF5bB3v0Byki9YDodnvWJTjO+iZMj0jz4j64MaS619BWKG7LG+voHYYq8okHBkUWnOKyjNX3A+ebEvUab5oi56bNI/SnrRDbViuGRhSU3vBaSnHJ62TKQVGDiIhKsseZ/1ArAk925F0uYB8j94VRuvSFgOjA4Vl+u8WK3l6qqdIoVAoFAqFAvpSpFAoFAqFQgFgxfSZBVwAOE9B5eURYuWJFAnpjzCi3fXuOLuzmbY4tyCJbc7Tbv1Oh/KdUICpGrkeDVFsTIu0add9QrlyQnIbDlHQyFpKUQzJVWidastT4RjaIc+5sMJipYGnPmPKJ1yJA3D5MDB5QEZDdesyfUR9EjNN5VESFFzO0S3ddpuOST8xbdqm44ucg4eCfNaHhTIbGBC702GKVOrV7cp3u+1uYXmmdzmfVoysDjHRpl7eIcrN02Gql9qoR7ROQgFK0zIX9xWEBWAdHci0Nbu2vSiWnAeM8w6RS73v0maaJSBlIfNurJhhFWelIuXbdB4Q/VKlvq1QDsQqRQ9tcr9Qn9fqpGrpybhL83FMrnW6z17CAf/YLJ53y6N8rgwsDBJkdfWYRSvtN988k9sTI5IHbYRo0RNzsnY+8vWMSuvNS/9uqsq4fPLAK7k902LKrFi9yOB13ldmspKT74O+EJiiIkiJTnng8azujxyQss2O3GdM1xytSW63N2wRZdlGyovYGSLK1a7+3DTG5s/FlNYUVnCPN0RlODK6PbenSEULUulat77YrowJQ0FtQcovbtiYy1vKd1aXPHkmLFYZVutSl5k5abeZ2RkpMyAXW1yU+gZE3UduTbAc0JnyXHq5G6kuW9dvyu3xDZKfz9K9Bhq8UaFQKBQKheLyoC9FCoVCoVAoFFghfWZgciUPex15d7sXxovzRZGbNCK3eD9QVJdorOaC5NuZ64rb7STRYbxzPWmLmxid2dzcMCr5boZHxqReRKE0yYM3uyAu+vOd87ldo1w0eybFhdfPPxQnLAugtqAAkGEkdJyhXfzsnub2SlfXQ5/lsetfj9yrFc7lRX3WIzqI3dWcs64fXK49L/3RJvqzOi790WLFWUvKBETTDTaECq3Xpf2GOEhiTYbyqbOimpllSo7OGVHOnoBo17SZ1b1L9eWAYjHlyOt5g5/yibFCjdy48Rr8BjF0fR46Cd17QuOehUHVGuc3YgWXaxPqH1MRCioiFefQgChAK1QmHB6V40TN8bivUB4r4ykOx6gMu9cJdE6mPvu3HTBVz6pBlg0SbEmQTu7PtQjFKcpZpkJlTHcWJN/jSEXWpLG6lD+zQNSuG9+PPnw4P1Zn6rvHwXiZPineJuFHzGXVJamrQprLRLsOBZSTjOgkWupBSz0W3Fqx4HU80aahFB7dLONkzw4OJCjfjAZXLL5eEaw1hfkPmaodHZA5ML5zmxR6VKhl25ZnknFtHrfkWRnVpSxT27QjAjFtK6hSmYAD61JdeTvMYIPWwGGh2+bmpQ5mXp6/EQU1Hh+hdcApWHsLcj+WaX7uqzrRZ7uuzu2RsV0ogr0MOlQ9RQqFQqFQKBTQlyKFQqFQKBQKAJcRvLEPzufEtA8JxFAhl6lpSPleTdxw1pWxTXKDL4gLrjogPtVJynm1SJRVMxa3+ciolNkwITv6qzWhYrAorrpeW4Kg9VriWkyIMhhfJ98doB34cTdz/XZIbYWAqBq6/4CUAWX0mRcskeiq1YAF0HPquYDpM1YRkbu802M3N/W3lw8na7/F80JjNadO53ZtXFyonOOMlSYVUonUh4SSCSko2ICRa+7aLK7miMbkzHlR50TkDh4hpVOd7q89l7mAmfpLST3YY6qBlZJE43JuppRy/UTBGhAuxled5Ye9eJIcxFLq3SPXdbVKSrS+zI6UIZ15UZrUh4XeatSLgzoacqE3Is5pyC764nxzIZWPmMptUq68Fily4gvz8AWU06lLNDgHn7N+mDmqynJytV15WEhQUD/wJ427RaI0ifoaGZb7rSyyejS73zCW83WIauhWpa13rheaO1yQuTxFew1iFOe14m0S6yfX5/Zb37Avt2/cQcEmO9J/02eEhpk5KvY3D2V1eLnJOQxzEzWiDK+5SuY3LTffZlgA2djkfI5ISTVNT+Qte/bmdmNS7OacrGlBkj0vE1JSG6LSgkFp+5hyW6aUM2+Q5iyvr/46ITYJQ7GOlGBDQ0KleWs5faFWIRrQzV9Lz15vfhGtt2lc1ITbr78lt4P6OirPueJWvtaqp0ihUCgUCoUC+lKkUCgUCoVCAWCF9JnFUldzBg7K57mvOJAXudKiwSGyMx9nQBRURG7woCNu4oEhOcemCXLZVcSVxmqkGlFWQSi7+6s1sUcHKJBXR+iAyFLuHVJLgNyV3U7m/gspYFrA6pwauScrnGONFTHFbcdu6NWBRd9l6ZMEFECS3etE582TcmykKn05WM/au0eUxszxo7ldI4XFyEZxv4aTk1IBCsQ1TIoFdqkyG8Vl9jXk/J321txOmtJnbVJHtE6fyu05R/P1SM2UEJXUImpukYJBtmg8sBKNg8KV5ie6krBLck3JYbFLFENMZRojcyZyY9Z6ecfEbs5O53alKxRzhVQwIdFXoLHuBfzjdmObgrvFTHNTf1pWfnKeJlf3HqnMOjFTZvQ1sr38Xl6Z4oCrqwFrgdj1pa9opPWB+uzMjPTD2DoJWDg5I21zwg1ZFt1FpNqa3Ctr6Hvfckduj08fze0HHnkht5+Ypu0QLZkPAQWv7bZkW8OTz8p5kMrc/77bduT2XdcT1XlEqKCTJx8BABwnlXFMw2pyqwSvfP9d1+d2NWY1L429VQ6MuxTGAFFOxTM1VZwndN82Cd54eP/rc/u5M8/kdjp3FgAQ0NqSELUdBLJ21WiNXrde6KgqUdvgHGusOPQjm+Zgansgkmcxs728TieL0p/pQjZeTcp0qJy8Riq3G669Nre3XyNtkbJKOi1Spl76HFVPkUKhUCgUCgX0pUihUCgUCoUCwOWoz5wb0gQ+6SJmSfAzoowiojkGx7Jd8nGX3fISmCzsyW75SkdcghUKAFltUJA32uXO9FVA168TzTBItJ5lVRxReN0Fcd132qLGsGnmzg3IDVmhIHY1Ujpxzih2FRqPUiDqynNnXnkYGFRcYLuUlDilb8tEU8xQTptR7ssB196p9Nni+bO5feI5cf+uo0B7Yzt25XZjVKi0ao2CkRULhDxlV0pUAwcga5NSZ54os8VXjuZ2xwUSSynYX5PGzBzd/2JL7o9VgqxWYvqssupUqCNDrdiFZVjdWKKs6nQpwKKjnyOiof3caHLvPQp62aP2CascGJJtDloqbct0GNNnacxqzGKaOSAFTf8sHT6HLabGylusJEdhSekrB0vqQFJ2WW4nWS9Pnn4xtwfrE7m9a1LWotlTWfn5rqyhCUWIPXtMKLijL4vK6bqbRf30D3cKNXfdGenv4y9K3jQeHzVaf8c3yBp91R6hcOpSHSy2pK/aHalDy+XrskTNrR+XufkDd8ma8fpdcs9VHierrBh8dRgk/UcurfEhqQlZlTbakEa59bY35vYrh57M7ZmDX89OR+o9sEJ4VlS/0ZBcpzYg/cDPZIYXgBnFf/iBTSlIM83TZFGem/GiPDdNf+xyCjza9rJ7h1CqN935jtyujgrtWrSNB4AEyVzBJFVPkUKhUCgUCgUuy1OU/edv6rQXfA4seZskbwp7TaouXPhAzFnF6RfDvHga+BcoxxlJ6A05HabNmV3xYgQRh64njwJdl7P29mgDbXNONrBFljZ1uVuqNWiz8Yj8UosGaKMwpYngkO+ccoTrZQs2zV5JGANUK9kNxBy9Pynu14DsFv0anKKULCODmWdneFjiQyVz8vnitHgA2Xsze0Z+1UzulF+moxSDqEqbtDnrfI88G2261sKUXGvuzInCOliOceM8F21KXTFP+w0XSQjAm7E53YHfZRRmfw18CwaAcZvU/VD3l3Zt/m7Pbaq25D2rsseT5h1vMrcUFyWhGEimZBukJ84o/WlKhylzNgs4uM37m8cTqtelwvMTrV2YIhgAkfOK8C/iwGsn8qhRaqPps+KN3bjphtzetSGbP4dOS5+1YoobtSj9/rdfezq3XzklqRjeeJX8Ur9ur3hnbtsra16FsqGHFMuo25XrztF6+o1D4rndtV02TLdOiWdh3o2zsCbnu3GHCCm+/2rxLPRaNCar5AEnz3vgpbFYGw9SPz0WObu85yN7bitW5sw122UNvO3tP5jbX3Mih+6xA3KNHsUsorkZz8ozFOShDxviia9URShhmKXw6kg3xM/frqyjKcX3sx15XgeWBRHuf/IOTW7cmNu33fnm3F63+/bc7iUkpAh5LeHN6yuHeooUCoVCoVAooC9FCoVCoVAoFABWSJ8ZUHwRwxsPSzZXe8FI6D2MNl8G1rnwKPy8oczJHL+hRxu3EoqB0aTYKRVy31Vo42UYUsoNrhddK6F4B3HMFImUiWpCr1TdRurG2Do6JnZAm77BGdlLMm5zSgbe/Lwa4FQCYYU2vZJ/l92lPiEj9Z+mDbZhmFFTeykG0Qi1TWdWXPE92rw+R33ZmqL0HIMS/6LCaSRok25KG3OTNsUYIjexR3dxHByighbduD1HdOpMiyhUOh/zZDHZTOly462Vi15GU/HcXN4peLN41hbdHrUZ0VVVikEUUnZ0HrvWi9dUkpme3d/Ut976we5yKtMrScvRv29T4v4v6xIvrUbJZnSzyr8pQ2MxFGRtv0gZ673aUFtGtGH3zFmJJWRJjLJt400AgC6d7/hZGevNrhxPFoS+OXzgJSn/rGyoHhmV+UgJ0zHaINqYxsG5OZmDC01ZM4ZHhIb7sd1Cgz07JXOvT4rv3Ssbvb//Xdfkdp1i8jzzvNRxfvGJ3I6IAt44LNeZGBZafjUhMYmIRrLFYyqk1XYwkjJ33XJzbqftHwYAPPg5Ekm8IrQnurJeGVojbZOfodIPCT2fOG2G8eJk0Q3xWsfUOYsBePsFzdnI7a6fIMrszXcLZXbDne+VU9dlcIW8rnEaLNry4M/ZS4N6ihQKhUKhUCigL0UKhUKhUCgUAC5HfeaUP6EnM6PP2S9tiykgDrMeuqzoZlB2vwdG4lhEkZSdJzdZm0K+syev2yQKJRW7QukjIr5+yNl55UQRqeVCom5qlKJkcCyL+VDl2A9Vvg/KOF6yQ957O/Xabg0yqzt3ZIVcywHZlnb7J+Su526NKZT+6ZlMVdIjSnLvJnF5T2wQWq1L6TY4DhSnBmA6rEe+Ww7ZX658JAUFpVhJiFKdoxuZdteaIVVck+MRMaXLsXGoz5IS9aBdg58gFkDqXPO2LB5PGX9UHozkgu/FXaFWaHj46h4Owc+ueE5tXkbrUd8y2xaTMjQhBWgQCUUUUh3ymE0ll/EVestQr6wZBQpUggCbXcyvl9py3a7HvLAyVBB2ZcyeePGp3I5dP1y/dX9+bMs6oSaee1niFE3JFEBCtGi7I23WnpI+OC1fRSVkuoWUjJyp3cgamZIq+PNPHJdznpE14cZbMqXZj753d35s57Aoqo48K5T7oeMSsymalDGzf6d8t0tbNabitVhnZbx5U5CoJm94MYVMh0cbMta/53VvAgAM1qQPH/zCn+b2zEsP53a8IOoww7QT03ekEgXb3jKxDAkmx92jdbc+KM/Q3Tsz+vLOO9+WH7vhznfldjokKkRf/UbtRaOeY5tdTnw/9RQpFAqFQqFQQF+KFAqFQqFQKABcDn1mL3Sb8a705YXPF/Q9hZU6BVcMKVVHVdxhlbooBeaJfumQX7k1R5mUKZs5Z3yvkotvoCFuPVMj119DXLxMn1WHJDBhdSArYwJxa3K2YT9DOf9BttecFC59lQP+GWNQcSpA67lIpf4J1adLHCWri5ji6Ad+PE+KtGeOv5zbG9YJzbhlvbhIRyhzsyGlWNwkVy8dNyWqhoSC+sVkN6nvZ9pCL7BybtGlLmGaJvXcslKVClNCpLwIOCN8VOwCX0305yGPHP7145HZXsoPOsdFaKKyMc1txek/TAljZr10QHz9oKg4LAXsBCtifM6UzL5dpngr/svfCUCu+1K68cqjEgbYMprRIic6EpA04DbjoKHUNgH1ckAKpFOHvwUAsBSEdNeeO3L7Pbfvye0DJ2WOPDtDKs7zMgdTK9QIU9UFjwcAQERbE1j9m/Tk/IsQ+0fvkbrd6paHcOZr+bGjx4Wz65j1ub3/ln25vXGrqMzGhmTtSShAbVxW4SsN01dDsnKW1hG/cG4FAVNcYo+4VCB33S506Ob1EkTziUc35/bBx0WF15kWdWLCgS5ZocZVKXtwhbS+hZR+i57jE5OiLrt5n6gF99/5vQCATbuk7kmdtp2QEo63LfC8T2j8B5SuJr2M7lRPkUKhUCgUCgX0pUihUCgUCoUCwGXlPsvep/wYZ+yY5+QuVMY7SYHrnNyEUVXccayGCmsUaIvcbck8B+ojtUQqCobIkhJtQBRIjfUSXDBiKo2ouqAq5SOy+0HkUlavcGDGElde2fGUA1+tcu4zAEidO5Zz3fSIGmPKMU6K61YU1JHSHCFuiit+gejM42dEPTJClOTYqLi5hwfkeJ0oTA7oyfVa7IgiZnGR3PJtUV8sLIrdo6CfffqH1WTkIfaCNHJuL0MToUrjNjVMdWANYEUdUqIy85kspprKuN3lo+zctoSmY3jVXQbd5avYyuxXP8dysCy1zSogiCIMTGZ5E+szkidsoV2c+8zycm6YtqU8ZC5H4JkXJdP63GnJObhwnSiB9t0sQQJfNyg0d3paqOWTEocVZ4mGnubOJM5526BsMdg5KdsjduwU6vya7bK2nu1JTrSnDh8CAGyiHFvzc2LHm67O7Tt2S92jmlCFi+elwtaKgjgxtFitInIqltZUXl/KcgFyjlFTMAYrFNzx6r1CmW3dfG9u33KLUGYvHnlA7EOSE3L2nLR3uymUbY/W1E5bnqd1Upbt2H19bl+zV1R+e667Vsrskn4JB7M+T4l281Ib0v0n1Ea8TnnUNi5cY1ayiqmnSKFQKBQKhQL6UqRQKBQKhUIBYIX0mbVA4tQ2gacAKQ6gl5a5zguCxZWpXkzEuX+I2mmIC5ZiuSGuUI4dK27diCowRjmBoiEJflUbZrcqKYloNzwHrOsrCZg95Lxm/OZpOKcYuHxxriW7FsEb3T0mdFNdorgi6kxWuHBfMd3Ud/VyMC3w9ygmWJtaYbEl1zw9fS63Q3K/VyrSZ0McTLMmLvcetVmrI67zTkcovITVZUWqyRJVFAevZBUIu3rLYph6ef/WAp5rGYW2N9ZKqaF+GabaSkqWjtfi7/rl+YOyuhTfU9kNFtampI5+LrPiE/IWgYsp9C4XUbWCiW0ZFTL5igQ0nFmQeRVzkzHFQMETe5Rjsj6ZBVF9I93eS9MUAPGxT+X2qZdE5fW6m3bm9jW77sztm6/fRdeUtbJDbXOO2rtCbVmdncrtuSmhbeYhdPlTzz8k9XG03dv2v0nOMSHUzzmiGJ868Lgcn340t8+eE0podOeHcvvG6ylv1mrBiuItKFkkEl4nKddmsISYzo/3nz20tiT0+dCwbC/Zd53QW7v2XJXbd76JthUsyLrbmhVl35FnJffd5/7mE7l91bhsL7nnfe/P7XFSlAW0BQW0DSZxAzbiZ2kJ5R6QijglOjgoJcj6ZS79+ameIoVCoVAoFAroS5FCoVAoFAoFgJWqzwxg3Q5wpn0M52ph8YEXlU3MQuUau8zofCm5rSPKfdYjd2NYEQqFAz9xUrSQKlapyXkiomJsyLnK2CfNMgGOWOfy2XhR8SgHFAe4YvrMekmM5HQckG0N6LP+JUzAikG5bqdH+Yo8QVMxxdT/w1MfcdC94nRTPi3KtE7K7mJyKdN360yF0uk7sXCqTH1x4Enrua+t+5/z61B1A3bvynFPeUjfrRHtG68u25LjUoaMT5kV5yjso4z2MqaMGvOvdPHKcGBGj3Qmi9eSYsq5SCFWRtP5JUs4OL6/NaS2LSy6bk5uGx3Njx87KwFrKxRclpVAtiJt2YBsB7j3oz8JALhlUMr+wW//l9yunRVKq7co9MmhB0Sh9swD35JKEq3RoJ0GNWq/05Qn7xwFjax15HhEa26V8xtS0L7U5V87eP/f5MeCgD43HEyVx6coUO2QtOOetwqFtH3XTVhtWNh8zU+YHmbKjMp7qrSCNQoAEPRzqZVsvzDF8yisyvNupEL2mKiwe1ulfWYjCYAZfPWvpXxdzj9KQXk56DEMU2ZyOMyPlTxHPDU3rQ2suKSx4s/GlS+26ilSKBQKhUKhgL4UKRQKhUKhUABYsfrM5rvkPdc50QZhmZd7yXnEvrCA752mXEjsbiOXLVMeTI+ErH6gioXkn0zp9ZDpLnbDca4rzvHUV1uFZWofqpf/GlqsKOD7S1jBtVqwF16D2yAtUVx56gC+F0cxJR4Fxi5ipi+K38s9WrYsqCBfkqgDVoKVKfzYTZsW0CbWozNJZUYSH1b7WKZ6Wa3G42oNhISAyW/O67cSCpsVo77yQ8pIULQyM64OnAAAIABJREFUN/eS6+dlmCouUZWS7RUpo8OWFQHzwv70gryV0l5MlS/jMquMXmJxcjajf9ePS/DEqCIqq0Faz2o0wHpMMRD1sGUko0qSQaGdTk2LsujsCTl33OV5z4FKqZJEz7T4MAcepONDXkBC/gLR9d55eDxl10pK5h3XhSl3DuoXd4UqOvvywdw+8fwaqM9gkbh8gCFt7wi8tiIVLy0wnOMroeP9VHLG8BrO2w2Kz8fN5qmEvQiqREdWpL4hKYAtKcESynUYgres0Hl4XXF/eOu19zlTZiC7OJMjM4WJffWtAK8G9RQpFAqFQqFQQF+KFAqFQqFQKAAAZiUKCmPMFICXLlpQcaWx01o7eSVPqH35bcMV70tA+/PbBO3L7y5of3734JL7ckUvRQqFQqFQKBTfbVD6TKFQKBQKhQL6UqRQKBQKhUIBYBkvRcaYXzDGPG2MedIY87gx5s6LfedyYIz5sjHmjlc7boz5nDFm7MJvl57zB40xP+/s9xtjrn+Vsv/CGPNRZ/9buu/7jDFb3HFjjPk1Y8wR9/lt7vhbjTF/dWl3fMH1P+za/JeMMf9jwedVY8xXjTErCqfwGu/PT7p7ftwYc9QY8ziV+59dfz5njHmXO7bLGPPU8u+28PpvMMb8ljHmXmPMx0rKfNEYM77M872W+++H3b2nS+tU1H/u+LvdsSP9a7rjR40xE8utc0G9KsaYb73aGDHG/CdjzNuXHEtcvz1ljPnLS2m3y6jrRe/VGLPwap9fwrX2G2O+/0qc6zLq8HFjzD0XKXNvfz1fpTq8lufpd/Q6+6ovRcaYNwJ4L4DbrLU3A3gngGMrqfSVhLX2+621MxcvmZf/jLX2l92f7wdQ2LnuRePHAfTTAP+qtfZma+1+AH8F4Bfd8fcAuNr9+6cA/p9Lv4tSvAfA35R9aK3tArgfwIfKypThtd6f1toPWWv3u/78MwCfcuWuB/BhADcAeDeA3zCGI2BdFl61Px3+AMBPXuxEr/X+A/AUgA8C+OqScoX95/rw15H1wfUAPvJqC/sl4m4AD1ykzP8N4OeXHGu5MXgjgHMA/vkVqs/fF+wH8G19KVom7gVwSS9Fy10TXuvz9Dt9nb2Yp2gzgLPW2g4AWGvPWmtPAPmvj18xxhwwxjxkjLnKHZ80xvyZMeZh9+9N7vigMeZ3XNnHjDHvc8cbxpg/NsYcNMb8OYDGxSrd/+Xj3jCfdb8MDhlj/tAY805jzAPGmMPGmNe78vcaYz5mjLkLwA8C+FX3Frt3yanfDuBb1trY3e8cfTYICSH1PgC/bzM8CGDMGLN5SR1f5+5zr8m8Pr9njPmaMeYlY8wHqe3+xhhTcd8xyBaVfnKh692b/gvGmJ+h038awI9crJ0K8JruT7qeAfDfA/gjd+h9AP7YWtux1r4I4AiA1y/5zh53n69z1/+0MeYLru4/ZYz5V+7zB40x6+ir7wDwRWdvcf192BjzK1TmMwA+crF2wmu8/6y1B621zxVUoaz/Xg/giLX2Bfdj4o9dWa57wxjz18aYf7Lc+ju8G0A/CVRosl+pT5vMo9xw9X0JwHpjzKaSpvsGgK2uHsYY86sm8yAdMMZ8yB33vM+u3e6ldv83JvNYHTDGXOuOr3f1eNoY89ugUJRu3D7qPvunS9riP7vj9xtjJt2x/W5MP2mM+XPjfmkb3wMx4epSBfC/AfiQ688PLTn/suaN64uHjTFPuLE74I5/3GQe+r8z2Zp4D7Xdx0zmffgigA10zV9053rKGPNfXdl7ANwB4A9dPRvGmHe4ehxw86JGbfy/G2O+BeDn3f/9c1/NfxNe0/OUrveduc5aa0v/ARgC8DiAQwB+A8Bb6LOjAH7B2R8F8FfO/gSAu529A8BBZ/8HAP/Q2WPunIMA/hWA33HHbwYQA7ijoC5f7h93154AsMuVvwnZC96jAH4H2SLwPgCfduXvBfAxZ38cwD0l9/tvAPz0kmP/Htlb/lMAJt2xv+rfo/v7fmST7K3us7tcXXa4z38JwNcBVADcAqAJ4D3usz8H8H5n34bsZav/nb8DUHP3Og2g4j4LAUy9Wt9pfxb3pzv+ZgCP0N8f69+L+/v/BXCPq89TAPYBeAzALXT9IwCGAUwCmAXwE+6z/wzgXzh7AsCX6DsvABgFUEcmzd1O1zwMYL3237L6L7/2RfrvHgC/Tcd/lK571NX3iwA+6o4tq/6u7EMABug7+93xP1lSl98C8EP09wLN4T8F8G739w8B+II7vhHAy8germ/t9yXd6710Dz/t7J/s3yuAXwPwi87+AWQ/5ibc3+vc/w1kY3u9+9sC+BFn/yK105Nw4wzZC8//VdD/EwCOLu3bgn67F8ubN+vpO/+O7vHjrs0CZF6LI+74B6nttgCYgRtT/ft19h8A+O8K6l9HtsZf4/7+farLUQA/R+f4EvX1f0Dx+NR5+h28zr6qp8hauwDgdmQU0RSATxr3K8Xhj+j/Nzr7nQA+ZjIe8TMARowxQwC+D9mb9uOuo+rIOv/NAP6bu96TyCbhpeBFa+0Bm8WyfxrA/Ta7+wPIGvtSsBnZfeaw1v6CtXY7gD8E8FPLOMd1AP4rssn3Mh3/a2ttz9UrhLj6uJ786xMAPmuzt+qzAM4gWyxhs1TLXWPM8CXcm/an4COQe70YJgH8BbIHxhN0/EvW2nlr7RSyyfqX7jjX8/sA3Effud9aO2utbQN4BsBO+uwMLuLO1/674vgLAL9rrf19OnbR+htjtgI4Z61t0nf6+yYehX+fS/u14dr8FLL5/AV3/G4Af2StTay1pwF8BcDrlnEPnyq4LvfhZwGcp/I/Y4x5AsCDALYj2wIAZPkSPuns/wbgbmPMKIAxa+1X3PHfc+e+HCxn3txoMq/6AWQe8Rvo+5+21qbW2mfg1kNXp37bnQDwt1T+bcaYb7pzvX3JufrYh6wPD7m/l97nJ8n+bQA/ZjLa50MQajeHztMc35Hr7EU367oH8JcBfNkNrH+E7K0RKEoylL15vsFVKIdzpf2QXeL+NuVJlJYLTpWT0t8pLj23WwvZoCvCHwL4HID/FcAryBaUPra5Y/sAnHTnuBXAiaX1tNamxpieG4BL6/l9yH4xet9xSJbcTw2A18bLwWu9P03Gf38Q2aLVR1l/AtlEfBnZQ+uZS6znewD8nyXfWdqfdfjpowrxWu+/Erxa/5UdB7I9Qe82xnyC5uNy6v9uAJ+nckv7lamMpf3astbuNxkl9Hlke4p+rfzWEMPf5rC0PfrXXjqeLoAx5q3IHr5vtNY2jTFfLjhfH7bkeFG9ltNHfSynfT+OzHv+hHuZeGvJ9191sBpj6sg8NXdYa48ZY37pEuvaxyLZf4bsGfC3AB611k4XfeG1Pk+/k9fZi2203meMuZoO7YcfkfND9P83nH0fgJ+mc+x35ucB/LTrZBhjbnXHvwrgH7hjNyJzBa4m5pG544pwEMBV/T+W3Pv7ADzr7M8A+KjJ8AYAs9bak+6zGWQu6//oFqFlwf0qi8om2ZKy65Fx1r3lnt997zXdnw7vBPCstfY4HfsMgA8bY2rGmN3Ifj0/5D7rAvgAsv7+B8utlGuXm5G50ZdTdhMy9/arldP+K0ZZ/z0M4GpjzG6T7Xf5sCvbxy8i86L8+iXWealH99VwDTJqwIPzMv0MgP/BPUC+hmwvTmiy/TxvdvfwErK9hTWTKYfesYxrch++B0BfcTMK4Lx7IboWwBvoOwEyKgPuu1+31s4COG+M+R53/EeRebCAbKz2H3is9Hq1/lwuhgGcNNley+XsnfwqpO02A3ibO95/UJ91Xpeyej4HYJdx+3vg36cH99LyeWTimt8tKqPzFMB38Dp7sY3WQwB+zxjzjDHmSWQ87i/R5+Pu+M8C+Jfu2M8AuMNkG/OeAfAT7vi/Rban5kljzNPubyAbXEPGmIPIOOtHL3Zzl4k/BvA/GbcJeslnfw3fbfrLJtug9yQyL87PuuOfQ8ZbHkG2Z8Db0e7c3+8F8Otm+VLM74VsFLsY3gbgs8ssy3it9yeQPRg9l6619mlke0GeQUZr/nP3S6//+SKy/vyXxpgfXGa9bgfwGHkgLlb2Qbtko2IBXtP9Z4z5gDHmODLK4bPGmM8D5f3n2vOnkD1YDgL4E1eW8bPIKK1fwTJgMtrkKmvts8soW0H2sHik6HNr7WPIaI+PINtb+CSAJ5B5IX7OWnvKWnvM3dtT7v/HllHNfwPgza5fP4jsFziQtU3k+vaXkVFofSwCeL3JpNFvR9b3QObh+FU3rvbT8f8E4J8ZYx5Dtqejjy8he4m7YKP1JeBfA/gmMk/eRdsZWdsdRtb/vw/3omEzpdVvIWu7zyN7Se7j4wB+02S0lAHwYwD+1Hl1UgC/+SrX+0NX5r6Sz1/T89ThO3adXXGaD2PMUWRuybMrOsHfU5hsJ//PWWsPr/F1fxvZRskHl1H2UwB+njjwK3H9o9D+vJLX/V+QbQT942WU/S8APmOtvf8yrncU2n+rDmPM3cg2i/7EMsp+AJks+1+vfs0UawWTxY8bXUm/6jy94te94uvsigIAfpfj55FtHFvTzrXW/uPllHM0wKev5AvRdzm+Xf357y6h+FOX80L0XY5vS/+VwVr7dWRK0uUgAvB/rGJ1FGsM9/Dfi8ybphB816yzmhBWoVAoFAqFApr7TKFQKBQKhQKAvhQpFAqFQqFQANCXIoVCoVAoFAoA+lKkUCgUCoVCAWCF6rNqrWobjSwuVmhko3bcE/l/TPu3I4q+2e2l8gEdNzY7nngbv+Vzy0FAyVxOYE9DhaKK2LWq3H69IgE5o5DrKO+NtrjqCN0fnO53sSOBSWO6p0oo5+t1pb16lCx4dEQC4gZGLnr0pemz1tpJXEEYE9gguPDdmO/P34tf3D9eNxT0Sdl+fu4bvx7UZ7VablepkTstCUzaS+QCxhsUdP6QhruEx0CaJnT8AmNJaF+6Z1Ni031wVdJErtPr9a54XwJAtVqz9cbgBfWwVA9DnWHT4v6s1WUMVqtVAECvJ2O61Za2N3TyCp0tjSWwbDuVcZyaam7X6Tr1mhxvDEqfd1oLuR135TzWG3/F91GISwwGbEoHd3bN+bk5tFrNyw4xvBRRFNlKxbVo+QS6shf1ltlLE+GUtVN5+60Uy1h3bNn8vThardbqzM1KZBs0xotwqW1V1P2mtEBZn5Sc+1X+6mNZY+Qy+qLwOsUmTMG4WGx30On2LmnQreilqNGo4663ZsltRyIJqjx96kxun+lK+YlQXjheOSkLXFKRAVJ1C+58IudLyZHFDxT/pYj+4IcR1bdakePrNsmyvWd7nkwZN2y7Ruo7JHVENJibnbacp0YvTsP1bAEfTuSq33pRorKfieWeto7IPR976VxuT9VGcvvd77wpt4cq8iD66D/5HY6KekUQBAGGhvqBSvlhTgOZBnWaFr8ZhvTyaApesrwXD0JILyoDjQEpH0g7bdojfbNtTF4kn39SosGfnpc2NoGcM4ufl2F4mNY5O5ubzQWx4/zlKik4BnC6wEokdazU5TrVQbmPKJQ2as5JCqqTx09e8b4EgHpjEK+7+11ZnRoy71LqT5PQC0pbxpeF3M+ufbfk9o4dWwEAUydEbfvUwQO5XYnlHjdQf7bOHsntg/PN3G5Xt+X2vmvkOvuukuM3vEECAj//hCjgzx6fl7rTK1jALw00FmEuXIb5gRAE9MOLXu54CPsvuTTmg+zcn/zE711wjSuBSqWC3bt29StHnxTfa1k9vRff/Dz8g5NPLWMj4RdZKsVrK/+Qoab01oMokl8yfrrN4nMa+oGYUH2MO2dAPz8Dml9eX9P3Um/9KmoLOTcAPP7k46syNxu1Kt54k1vLSjiaiNozpB/QqbnwgQ8A/dvvj0UACLwfaNRWgcyXKKIfTGHxWh+B13dZGxLqtzSRB33Zb2duf29M5X1R5uUorldCfZjQnA2pUSN3+P5vyjq1XKzopchEAaqj2cK/bkhOERh5MFUWxO7OScNVqfwiZanoBZldpRseHZCOqFGjzCxKwy705BzcKbWGfHdsSF5srt8pL0Lbh2TAzE6dzu3mgvzCNTV5QRpZLw/VTWNjuf3KC9ni/9SslLW0YPfoYd/YJC8/t6TSFi/E8os56cg9LRp6u1wFBEGAai27dq8r9+3/Aih+vw9owvEE7hfnCcDgYc9rVI/em7zT0XlCK20W0sMdFKQ0JY9lCmnLhbY8mIeHpB9MQKmN3Mt3SAt5ksiLA7/c9ehll7PtGCPlg4gWq5L2uNKwro69DvUnv+TSD4+YvJVBVbwz/IDNH3DkXQPdezuRuTZFP3SwXubadVskQ0BlQl76b95/a25vG5c67pocz+3Tgcyfc0b6ynoPNbms5xUr8OKi5AGT0h88dgMa//wS4D1gVwPWyoPb80ryi4gcT1H8glf0oOGXHH5pKvbVAgG/S/B1eEjTl/klPE65n/ha/GOj+CXNCxnjbAvyFnovtVy0+KXIP5+YBqs/Nw3kR5L1xiA92LlBaQxaW9xW/ZdC7nse7PxympBjITS8dtKLJ52Hwz7HXl+RbWhNYK8/szPUR3zZ/suNLXnh98Y2j3kqHfFLVkz311+HVrBBSPcUKRQKhUKhUEBfihQKhUKhUCgArJA+qwQGG4cy99TeSaF97OCm3H7xlFAVTaKgWosnc3tobCi3N2/P9hM0ekIXXT8h7vdKSyiJLz/9cm6fT8XlX4lGc3tkTNz4oxXxJ0YtcbE9eX4ut7fStdbR/p6NOzZLmVHZozFBe17mJ7PyL587lh9rx0IX1MaFaquOSnu1W+KgnD0jVMfBl2Rv1k0Tcn+rhdDtwela2TxrS/hcz3XvbbxgN3Z6wfd8foNMdguzy7Uu7bt9vbTZhrrQNqeGqY/JZ2x7cs4mUWkGMrYWF2R8xomU6d93L2Z3sSAkytAS/RuTgCCKpO+rA9R/dnWpUCCj95rNbI8Uc+9BKPX2yBTijCohbz4n172jPAzts0p6Mla6sZSNicYcHpb5vfcaocn27Hl9bod1OWez9Upuj/ZkrtWpA5LSscP7U7xdF+57vDeFPiaaic/B9BJTR4ba1K72T0pjZL6VbJj1hAR01CODPJbPXHCQ+9U7H89pspPC8/lUWpk4IwiKaZglRCZ9lffHXDgOjeX9SiUcKtfEq1jxeF8tWAD9/Ke8jnLPBSX9zNSu8foucOem/qFb6RFVzs2d0D5A/5LeheirvI+H9p0xlcl9RVRdrSJbSSzxZ7Fr8xIG1ttflXr7BKm894ihLQ/9BisZB68G9RQpFAqFQqFQQF+KFAqFQqFQKACskD4brFbwhl0Z3ZQOCO2TUKiOram4zudJ8nfomLyHbd8m1NQbb8xkv8N1itdDFNXRl0/k9o7d63L7+hGhKoL61tweHhP3++CMSK5PnBX1SliV77J0MSSH3tSx47k9e4pk+ES5VEYyddvVV12VHztD9AsrqdaRquloQKq8QTn3REMol6AidVwtpEl84cESF7kfm6f0CwCAgKTx1tMysKyZqM1Iju/evTu337hV+nU0kv5rn5bxMzYiCsNaXWjUg8eezO2EKtyeb9FxrnVywd2wxJ8pQ0s8AquVWi2hZRPIOKxe6ZgyBYiTGDPnzwIAAsPSZbErFaEjw1DsCke3CJlusu6YuME5NlG7Kfcb02CvDEj/JBTa4tgxoZlPTb2Y2zMLMsfnnl0vx70wHVRHT27PChpBv+5ML5RElViijGJKw9PnF5ZfbfhEE1PbxRLmErY6P+4rIYsVXGW/mQOPqiw6ix8Vgct7dsm3PRqsKA5PgboQ8JVNvlqLaNFiRf6KaJZLhgHg4uQlpWq3YjoU3j3wupOtq6nHK4vp9TLdYuyNY7KpHcqk9N4QoZAIPSpTZfUmNzqtD/2YhLGn9KT68h+kqDMlKjcOCZHToStIeK+eIoVCoVAoFAroS5FCoVAoFAoFgBXSZ2GQYrCeqXeeOCEu74FAAq4NDYrdqG7M7e1zsit946TQYP3IxgPDpGajYIKbJkXBtXG7UCvr1ss5xjeI+q0+Jq57MyO32TNCTU2fk7qfnBaarN4S9deLhw/l9nwglODV6+Q8i7Xs/EenZ6SOExJwLqRgjM0ZoREGhydy+6q9QpNNkDsxoYCQqwJr86B8viKC6ZPiQGPeaViVkwdZI1c5RX82FATQ0Ht5bUCCY14zKP3aOfCIXGjzvty8fe+HcvvBY9/K7WenJfJyK5K+HwqlT3qh0GeIhcbs36v13MUc2Zd995TUgoNsWhm33TZRP5UVTbdLgxVFWUpKsF6X6EJSjjUGZF6ZUOyQ3Og155eOSOrR6QqN2e3KGK01ZP4OUk6WZiwU23NHJcrs4pzU5bq9O6U8yW3mmqIU9KIQ09ixrD702JesjBfFmukfsgcapODpMXVQTO3YFbjmLx3Z9bw40CXUmBfJ+MJTeN+wJdH/PQUZX9W715K1oSRepB8F/MIgr0tP70XjxoXgPuDAkChpFz9IY3GAy3QN+rISBZh0zwVWhnpZhryAoMWKSg4aGruAhV6kaKbPeL7Q/OV+sKnMnTSR4522rGlxLGtJldJjsWKTlYWBp0qj7AAxK9dwQd0N07oB2TGPUqLsvHnNVFp2HyvpV/UUKRQKhUKhUEBfihQKhUKhUCgArJA+a8U9HDxzCgCwMCP0wMmmqLx2T3ByNnGRbx4Ql1jz9FRuP7GQBWfcNSH0UpWC9r3tqjfm9o7r3p7bI1uk/EBdlD6Wg+95uUjJ3Vu5Xewq5WyiAJInjr2U23OLp3L7yKf+KLcfe3kaADBMgR4nKMjg4pzQCyenJfDkmaa0RaNNiVG3yH3vGV5d9ZkF7/4vDrZYlnWe3biFWcqp4TmfL+fVqpB6YeNmokgDoUzszHRuz3aO5vaWt7wrt/c0hG57+ktCY3ZmhbbphdInoSEXMNFdae5SL6EJ+T7IRewFRyzJ5WN7lCBtlVCt1rF1h0s6SVHcbEL3mBJdSLRmdYhy9IXiLh+vZn3RHZbP9934utyukDrvmq1Cm1939bW53aQ8hl+YfyG3W10ZCxu7EqDVdIlibUl9mcIDXbcSFqvF8uLUnZwMkxP5blgva0nakrY7cU6S0MYp5VpcA8FS4BSZPu3DmiLKC8jJP6kNmObrUwwe1UBn85RfXiBHOVohSjqi8RNQu3Ki3TIla0Ljs0M5LDm/YFqQ/yu1fEzOnNpiPo4pqaLz+bVaPVSiENsmsnUqLMkyxxpdbxXhpLbUbrFrtw7l/WJKsUtlOfdZSpFHe0RNNSm4cYeSvVIMXIQVXr9lLFRpLa+RWozvtOcFAXX9yX1CNB0HSvXz1zEFxwFG6UKXoQxVT5FCoVAoFAoF9KVIoVAoFAqFAsAK6bN2O8HBIxlVdvWk5Aw7XxGaaGRMfFm71m3L7Q1G6KjpRXLhjWau6/UVUSBN3i70yFX7xBU/RrnJ0jOiOlo4cVrOd/qcVJjypoF33Q8JNRVu3JPbjW2Sp2nzHlE7bUp35fa6hKi6Zx8AABw+9mh+7OScuO+GK5THKRBXfI05pVNS9ynKnTXYobqvCmzuagzCYrUJq9JsmUvTc/b23Zvk2qQAfKx2COvSl9vrQl/Y0y9QeaLsmtJOJw5+Suo4Iu0UtEQZxdftxhRIcUjotrQj47bvmvXd6UQLUPBNEJUSRhzgkerLLuvVT6+EWr2Oq665IasH1dsLTEgu7HpDFJXrtslcvnnvRrIzWtMmco8JpK/GxoT2HB0mhctZyWXWOSDz9IZROc+Bl4RCn36WgjoSnZJQfqsu56uiXuI8Sd4vPVcdpndDVp8RHZeSYnTrdbfkdp1UdAttzqW3uqSLMQYVlzfKU0MWC65KKTEUqL8iOsbU42BdVLVjg7IObRiRPp4ck7mzYVzs0VEp06jLuGJlYJfWs+kZoblPzop9ZkbW7ikXiBQApuezMi1SJfc8iriYGisL4OdRkoUlriwqUQWbxjPFcaNSHBC2Q/eQEh0VUP7OgHM19rL50KXgjW2ym/SIOU+5Ns/OyvaE2bPnc3tmTtbOFilnE6Ln2rG0f9SR4xHRahODMo62Tcg4GicKPnTjrk3bVXicMwPGQSUTpleliK8mdP9/88ARXCrUU6RQKBQKhUIBfSlSKBQKhUKhALBC+szGKbpTmQttsS5uuB27hPrat0cCsdXIZdrdIHmp6qm42CYnbgMA3HbH2/Jjo/uuzm1z5JncnnvsM7kdTj8r9erJdTg4VUg5mNhdnszJd80ZCdLYonxn6fVC29WuvjG3N9z+ptx+544sN5f9srg4H3vmS7ndNeIyXCQqbXBCmr8VUzC8trgHw2Ghl1YHJnev++7k4gBt/BodeFSaHC8Kasfn9gLmDYv6p8EqMKYNB8UVz+7S2TPiGt01fHNu37JRqJ05Ui7NMSXTEne9l0uqfx1O1xMW1z2oSN0rUbE7PA3J1VuUY+6Kw8Igu0+m9OpE6a1riELs9be9PrdvuU6Cnw62hdaqulxlrdOivpyZl/F6mqiAeK/MtUEratSpB7+S2+Z5oUd2kyqOPO4YS6Wt5qndThK9MB2K8qk1JypDT3HTd7UT1RmQ+53H9uCg0Pw80thdzymm/n/23jvekqu8El276qSb+97OuVut0GpFhCQUQAQDNphkjI0fYBsc59ljjz1jv+eZ8cxj3pvx+Pk922Ob8fgZ7GHA2IwxwUQBkpAECiggtUKru6XO+eZw8qmq/f6oOrVW3T6ndfuqT4Nhr99PP329b52qXTtV1bf2+j6/Tev1iHux1qKRUP/dcw6qQqwzRarUYrsdiiUqXFfKGrNtFemw7SNcqy8dIDW2tZ/za12JfdAXUWXsL4h6VJILWlGozZZoHzGsw6ECyw8N8PwvzMXXPTHHcTUpeS0RcSxlu0SpU3TExYjDmfMMxgbjdi8IZamqwZbUtSGLXT1SVRbtoaT9/Tz7pOmRrjoyy344fZp5BvccfH+rAAAgAElEQVSe5hycPkV1L0T1mQnIq/nJlNeSdSUv1CsarOP6fj7z1o7wXteviseUJ2PIkw7Ky7qiClfdhtAUJZrmXmsmdf/CvdzSslQ4T5GDg4ODg4ODA5brKcrlEa2KN2UeKfMLbfUxfpU/PMm3z3l9s11Lb9KuTYx1suvmHwEArLyUX7GtJ+9M7dojj6R2vsXNd+EYvTd91+5IbTMiG7oGNrBcQofbGjeCosyvnMoUN0N7Lb5dB0cl5UaR91HcEG/Gvv2Gt/FY2UR416O8jxUjW1J7nXyJFfr4Jdbq55t4KcwEWeoBbPpFEEkQnkyMDHl19nXTdfY0Kdqb4qJM1m7Z9Ovzi2LdINvAn+VG21aDv910E71y60rsgwMPfSu1F47Ti7Fr467UPjj+MI+ZoXdDm1XjnrQ/jjSWUiZIi2z0DSONhyNemRLHXiDpMMKL8DVaqyxg92OxlzIvnqyBAr/Wbrn8+tS+dgPHfeFbn0jt8p7Dqe358ddta4xjdHgd59TaKzkHi5dRsNCU9Bwb3sJr2ts4vyL5Mm1mPIWSNsWjV6Ms3qGFAsdReZ4eg6kZbhydOHEKAHD46W+nZYf2PpnaQYFeyL4Nd/B88rVdbdBzrJOh191prUUr2eDaLaVIxgOraXVkyKqnc6QUf6mvlq/3S2Wz9BXilb28wPGz1bCfRuoc06UFrsVRhWt+JMeo2AAS12ZI1rzt0g8Dck9DPufm4EDsDRnOse5HfNbxxDTTM1Ub6unuEjdMxSQXIehUCIuFxFM3JHHNSl5nIQskZo8nMbzUUxMlnjc7SE/RiRm2ySMvsE0OnOAaeeoM5wiaInwRUYN61VScoF5nORzNJo+ZaNIz+/RRXrcm6YYuC2L7convNzYs87HIua5iACPplTROUUvaK0jGnP5uqXCeIgcHBwcHBwcHuJciBwcHBwcHBwcAy6TPfA8Y7o/9ZkGN/rMbtzN7/cwkM9AHICVWGNmW2uuueyPtHbELt/Xc19Ky8gN3p7YZoott8DbGL/I2vZrHrJJNyd1SNguMJd2mO+0GZMMeqs/S3E0qpvE83ZJ9N/woAGD0SqYNed0PvTe1g8nnUvvB3dzQvU/Sf1x5DesShKQdjgkN2RNYkNfJZBxWd6WPTjCyQbAgv20msYGym6v59/4h2Xg3SBtTdOn6gxtTe/3tb07tjcOka6eP7Evt2eMcb/0rr0nt7WtXpvaROdIg5VBpB6K9SdqTe/M1hYSMk6ZSP0Kroc7x4wlnFgW9D1QUBC3MJCl4fHFzz+bocj4k7vq5Izek9sYG+yKAxKu58Z2xceO2tMwbpbveG6ANoewKZEeANZtpd2Eq+m239uEPRrtlUJd+aTV5nvHdcVy0u08xPtqC7Oju28Z1ZeUOuu6PlDk3W3KhXKbuve/PNFN9h0z3Z0PGtHzulmTD6qrheC3e2C/xwYQn2SKpHTaL1mGlUBM5jRNT43xEg+uWJ5SZlwmsJDF2AlJvK/pIq/SJEKAkdFtfLv5tTto9N8o+g2H5iWluJG40eG4r89QKFfoSskIsGa0gwsnpuL0GhT4b6eP8aQjtFNS4jpRy8nyS9XNkLG6rcdmS8MRxppJ69iS3iMxMkmL2RPNhuownpfI0PFtRxpaR+R602P7lhsY1or0QsA4LyfGex7pvl20IIwPSz5o2JPNs9zqW+2b5IgjnKXJwcHBwcHBwgHspcnBwcHBwcHAAsEz6LGcsVuZjl6SVbOPr15OqqJa541wiuOO6K0h3vfwqKlhw6F4AQPlbjGfir7g0tYdeSwrF38RyiNuwG03WFUoLZTxyUi7xb4rb6c6rH/hSalfuvwcAkCvw/kckTcJ11/Ac+3bTjb97gtTY9JOswOAA2/SKDcpBXHhYAGHiJlXBlclkuRaaQOOfCK2kIfb9xDXsaZyJIlVml0hqmME63dxG0nn0bWeMquGtjHnlD/I6625kKobaOONCVSY49m7YxLY/coYpQp6boZu6JWkI2rGHCnmOq7y45UOh3fwc3cItiZFVa2q8LJ1iLfQa1loESUrrUKdDk7TIE2dII37xNGmiX7iOMYsqQn+Pbo6pFn8zqSbklvA9pdfPL+X768WP6ZzDPYuilZgux+L0IhunqCIdvoLjZu31t6b2Uwu856bQZ0bGRyhzwSRxUWwvdWidKEXlxrpQeJq9fvWwUGWDsb1ZlD0bpSE3yrxfqTSVqIaUJjNqa9t0oTg0wzlElYwWz1MqsG7rRZWWT8aH1ZhN8ggLh6kYbASca+PTpKGCQNPudI6z1ivYKEKQxE2ryrpQFQqyWpfM9A3Wu1/ine3o43Pm0i3xtovped7v8yeptKxOcX3tl2e1kf0OzSb7IdJUKVL3UCjQUNMHZRpO+0XmTMTrzld4raPj8RwbLnLbhOxIwLqVHLe+3H9exmhR7IIoEYtJjLZoGept5ylycHBwcHBwcIB7KXJwcHBwcHBwALBM+szzgIG+5KeSbffeh6myOnCMruhDHtUEb1q3LbX9I1R2zdwTK7uKPl2Dg2/4UR67kdnqM+5joVy6+kCVCloKw5bxSJNGyW2kUmfF6+lOnvvCPwAApu78Qlq28s3vS+3LX/vLqX31AQmm9eADtA8eTu1VwwymZs9IGPueoX3DdHlmkkxL+HQ/T9e2Lw0VGHVjxmOjpZKOQbo2d2xYldr5I0Kfldj3K3eyrcUrCpToIt948+tTe2bPM6l9Zi/bOBplkM07rqAqbe4pjr0zIYMtDq2Nx9lQka7ruRNUDM5LwMa8UEhWMkerWimydGubiyBx8TwfxVTNogNZKUC6sO+/76HUfsuNbPNV60lxzj11LwBgxVaOS2891YHfGxD3/gzp0/ndXwUAlOpUOuV3CO06zPs4Oc41S2lSXW40CKntcX9aC0TtiagCLqN0tqRfkMWtT1RbozlJp5TIeFaL4mylpD4SLRfyEszVBKSEI6HSjB6jn9iZ1CJSXV9lRDSt0By2SeqyIOvNyiRYZ02op7Io4ebk1LMSnLJS4/yenOdejiy1chGCN4YRZqfje1PqLpC+rUrG+Cjk2lHKc85uv5TpeNbuiIOiHvkKKbOjx7mmFuTk/RKMM1dkY1VkHNclAKNWTIMkNlS5Jg+KoAuVpkEyA+n02UpcfugUn3FNUd9NTbGvckL3FSV9UZ9s4SjItpdcQtfXGue/ZcF5ihwcHBwcHBwc4F6KHBwcHBwcHBwALJM+i2yESpLTaUSC033nFNU9p6dJq131IwzSeOkOKs4qDzKDrT8Xu2eLt/4Qy4QyCySb9hPPMndR4yTdpCssqZX8MN1tGyT799BKHhOIq/DQSXFVar6qKl2so7tIHwxvfEVq998S1z34x6+mZdV930ntkR23pfaOa6jCuv4o7a1bmTNqsMhusdPMVt4zJH7vjPMz6pwHLRR1guY26+tnfqpC4tIPxM+6cZTKpVz5FO0WrzO0lYqzTVe9TK4q7lcZb4W1zHG24Y7Xpfb0iU+ldm2cgcw2XM7jd65lHWYn+G0wemmcj++1u6j62/eVv0rtJ0/Qtd+QzOsa/U1phPNlbl8q8oUCNm6Jg6h6wlt0SayOIKAC8tFjDPT2lh1szzMPfg4AUHyBwTIHvsfoM1uhq336rq+k9plnYrVnn+TD237LVal93wnO06mGyGQNKSdP8mKpqqpNbfW2X9tnVz5bVT6SbV1Fs7KGDEvku8GkzkOiAhvxSKuU9DNZc2wJrZPJw6YqM80f1plJWcRFdr5WKEEAcxJMMpfQJivkPleJrTTgqChfB0oMjjhV5tqeDeTYe2o7DCPMVKqJzWs3ZA1sSXkoTTU6RGJzQfKDfeK+eAvGPXc9yIMXhN5USkvO3ZdnwxnJbq/5GVui4tS4s5EepIE8IdC8ctA5I6q3ZCvCRJl9XPD5u2JGPUvTGL4L5DILLBssSMaCBo5cKpynyMHBwcHBwcEB7qXIwcHBwcHBwQHAsnOf5TE8EKtTclW6nBs5+rhWrWCuo9deSpXQNnFxTR0h3daXvzI+9wYqQyDBplTFU5X8KFsvZZDEMY8UjjdA1UJpgBSbiCUwf4gU3/Fx2tdcTvfkwSNUrUwc4THXS54kf2WsrssN8++Vw4+l9mCFqqdLLydFhEOkcCZGSfFF4rasnlDp1cVD1pks7k9RouVLbOPBIcl/lQQyLIoL/9I1pKMGj7Hfwzzbcf1lbKf+IZ575tSe1H5inK7TS6+9KbW3XEPadd3j30rto4+REipPkfp62bYrU/tQhUq0oh8fM7b97WnZ7bfS1Txz339N7YMzHEyBlQCQLXXLQ9B7Ai2Xy2N0VTw3NZieZq/zxOXcEKf3A3sPpPYrfpz0ZTHJDxYcfyEts/PXp7YRteR3C/VDrNuZhxkANpcolYavvj0tOzDNNWjvYSpfWqFQCkLneKaLqiZRvvaKeDEAvPbZlZnqEky1IPntBgukRAaEPutL7D6hl0qSv8qKsqsma27m81mUbXr9TOzcbvkmle7wlfpQioXFLaG4TD4+piC8kuYQG5Z7GhLl1IAEqiwV5P7k9s6fZDl/WAPYJIdZqyU5zkQNKssrfKGa+iXAYrlGJeDDu+PAwadOUznpt7hGWpn5gbRJQ+ionKjSBkt8VpZtQ46XOso96bDI6UwQVZj2Z6RBIBOzLgdUhe6qShtl5qCMIQ34q33YSh70Ydd8it3hPEUODg4ODg4ODnAvRQ4ODg4ODg4OAJZJnxnjo5iLXebD+YW0/KqNzFVSihj87bUvJ1Xhzx7jeeZJZ/RtuwUAkN9A2i17Tbr41ouSaeUa0k794hrN5PHKpPESd6xlkKubr2ZAwf5R0nCDt25K7blTpMdshecpbIxzsQ28/JVpWe3R53nRsgRV6yc9mLdPp/bRBbZFTgKlVcVV2iu0czepSsB2yzIlbsxCga7WwSLt6Vqsfli1ZntaNiTB87w67ym3iaq79dcyT1mrynFy7xc/ntp372OeoJe/lfX6idcxh9XWO96S2pPP/2Vql08x/9XQTiobL1vB/v7mE7GC8G/KvM+dl5Iquvxm0kpH7mTgw3yOYz/v0e1ck9xQ9iLQZ8YYydvGe8h1CTSobEY4QQrq+WNUZb1ia0xrNh5jsNHKbv598JY7eBIJttdrRDWuPZNPUZE6dYKU/tDqnXG1xrh+PH6QgT4nhUaIMqouUV5JG2XEWUl/9qxXjZw86qwk1BxTOYlyWiqKGlSUS/mxseT/DJRqRzh2W0KDtzxSJjaQhkLnxdVGqjhafCPtw+U+RC2WXaTR8Xg/WW+iQFRGUxwDvjxPchNc2/M1rtWaizAQKuciiM8AcCxZmXiqfPMs722FjMfNFa4p4Qzv88BETP9OzbJMaTfNWdbQqLKGbaIU5IBQsJGo0lqiONOgl56cv0/oUCNbK8oaQFECP7afO6psqwnFV5H8db7SqHIdJcfCDsMpWkbHOk+Rg4ODg4ODgwPcS5GDg4ODg4ODA4Bl0me1ZgvPHI93u2/NkQoJI7r4DmtcuyGhEM4wYKPXIhVi1iRuLhExZSCKrPIMc1s9/DTPURK105YrGSRyg9B6OY/Bu9BPSqdQZH42df2WBsRVN0aXYFZFEVNv3iiDwhX7JBDcKVG17NiW2vsoPsOXTh5M7aE6A+rVWwzE1Su0mRWLJezUF/duUxQJk3N0Y7eSQzavlaCZE1Q2wWcnr7qcdFRpiG2656FvpPb93yY1Uq7TvXvf5/82tUfXbEntH9/1mtRedx1zAh36FimfcJxBMXetJ0W6+1hMER175BNp2cT+e3idYd5/RdzReVG+SNos5AqitshQEL2BgYFv4vGmubkyaio53hc1aBRwbnznwFOpvfN17wEAlDYeTsvq48dTe0CCdJpe02eiSJl7hAHrTt75jzxEXPcrX/0GAMDzDaoQj89SqdOMOBaVPssrLaTBLiPNNXZ20NMLiTj3WXIt25mk80RdkxdaoSCKsjGZh9t2xfT9zl2cd6u2ce7kRkTtqgMlE7CxSwDGTMLEjtU91x+6HN5hTZKivkk+f4YOk3Kf/w7n+sHdu1M758/wx4aUU0aW3CNEUYRaNV7P9bay8QUluKEEyg1Eca0q2pnxeM4GTclrJmezGvhW+6fFOet34X9LolpEP6m0hRrXMSOV9yXwop+XLSMet1bUq3xHiJL70yDAVrhqzYeXK0m50K4tuX5LqLd0Ljj6zMHBwcHBwcFheXAvRQ4ODg4ODg4OWCZ9FgQBzkzE9EOZnjH44rbu27YttVt9dO35ESkrSHCoyE/cYF1e0zyPbjLZFI+NWxi8cXSIiovTskP/1BBPev2IBEaUhColceF1C5cYiTtTaaQ2TE6bk20RVumG9IVeqBSZ+6xc5v2tHyTdt2qUQQ+B3egNznYx+hJ8y+viFbcSJGxB7FUbY0XZSEAFCBYYJC+3Zmtqb7nm5SwXd3b1CJVirTrrYsSlGowfTu2pI6RUqzdu4/lvY1DH6f0M0jhz6nRqj4wyaOQtm2L145ee5/Xr06SKzkzp6JC6NOnGt0IveNKOXRiQC4+E+vG0HkqloYNMA0AUcP4ce5506LPbYrrpVdeznVqPP5LajYOko0q7tvHcuWUtLx0gypcpjqnZR1kHb57ja/Ouq1N7shVT0Q/sYw68apOUmZUFJxL1VGQ02ZOMPzneJH1reqQ/M4a51pSuz8ZIVGWX17Hcy3PBzA/Ha05+DdXBuY1cQ3HxxIMXBH0DnIMDDaEPRw6ntgYn9LsElYwuAn0GUF3WCkTZpyo4mac1oWqnJR/cwgxp7lyyhSHKLMw0u43NQAMmqjpMjtedLIOyPSASJXirpjkfVVkoW0b0sZmhs8zZZXLPRVFTFgsyT2UuiJgNgbRpMaH+vC79fS44T5GDg4ODg4ODA9xLkYODg4ODg4MDgGXSZ61mgImjMX12ptSflm/dwiBhOVGkaFAvKwH/4GmwRc3OdDZ8n9fZdeUN8hd5r5Mt/Y1x0im7p6jmikYYpLHq0W0owieQRMhCqZBM1Lv25UUWEUkARlX+QFy560X1cVONdM0lW3ivI3ntoju71OylwKLtb/XFXanBLyONrqXB/iJRMBT5241rY9f82DypDs3BM7aDypfh9VT9IaJiL+wnhZgJeih97Imr1c6Reosacsw2BoTcfPMtqb3wpbtTu3aaMsCtm2Jqb/s0635wjjSrKkI0iJlKODzhGyMJQHb+WXjOHxYMWJZVlXiLjkosdd1reY101InjcS6x1nXvScv6hAINZ6hUa52i0z23hgECTfEl8DKiwJx98oupPf4kc9y1+hgotH/7W1P78fG9AIAzZapUQ43GaDvzDsqUZ/IuqSot+W0v4/557QCRGcFXl8Cq0se+15lyzlXjeZI7TTUeSrL2ruW8yw4ZTcrVOfdZJnGXQtdN2+V4PY/fpX/a/aa5BSW2rZmTrRkBx4wxkrdL7kNzyEXd6n6BESX9pYqzDAWqQR3FrsiAXKjKmpKuzZ1HYWYJ0KaU4lCOqouiWKmnopxoMCdU1gCp2YYoQ7OBUKU+GTrr7KDBWpeGrKNhyHO39HkkERs1p2OUzk2nPnNwcHBwcHBwWBbcS5GDg4ODg4ODA5ZJn3kA+pKfhhFdr/NnSH9Um+K2qkoQqhzd7lGeipBwIfEn6kb4jAysm8u4c3HfMK/fL+42ddVFQtnZJexS98Vt6GVeJ+Pzh3UJUil5g/xVQktUSEsUDzI/2vwkKb77Re10w7YdL1qvlwLPeCgVYmojzHgarR6kv0gtX9zoY2MbU3uNF7uuvbkzPHaIapeN1zL/2zP77qe9lyqmw/vYBvUunm0Tkdba/8RXUvvDZQZuW3EpAzO+7apXp/bIU8w7N32E1xpZHdft+vVU5JwpH0ltK/fcEnexNaK2yCiChFbLXdxvEHUdq7omS790zvEVCuW9/4W4Hx/fw3b4IQn+5y+QtjjxJAMqrtrM/HKla6gIw7mZ8qQyNGuHSYUf+Dops6jKQHxrb6GKcXIF596ze2OaX1gBRBogTi6pc7rT/I6h1E77oF4SaO0gdEL7yF/VDoSqrdaEcjzB8X0syRc3sJ/jf+0YVbD+ILc3BEI7GckZ6QnNbmQLhC6hutXAZNZNDYDLgeBpoEAdnkKl+YWh2GiybK5C2vzwGQZknTzDMRM0eUzYKRgkFtM6vUFkLeqNRKWbGTKd86AFUqV54duqTa577aYNheLNbFfJML+aw5KmVqWl804mjW4B0RyjGrTWy4niWvq2WdOEZ/YsW+lpVd815IGklFlLAk8qKZ+TsfhS6FDnKXJwcHBwcHBwgHspcnBwcHBwcHAAsEz6zFqgnniwmqIMma/RZbUmP8byaTq5cttJnfhDe3jSidglaMvishtl9dTFVxEXXE52uUfibxyv8LfFfiqcopB2OEtqJSqI+1aCQ87NsT4zokLatI4KMbQS9dLcfp47J+7u1aKKq+xN7aeefCy1v3mMCqia0EIn9kquth7A8z30DcaKoWqF1wqjzpSA50sgSlWcbWNbrq8lNKrQFKPbSbcMbWRAytkH2WbTB0jPmAUGD/RUFZSpjbYrqTczy+uWQuZ96ttMCmfbLa9M7fkTn6F9MqaCLttK2vLgBKnNZyqsS9Hn9aviIpYUPBlFn+YZ6x0sTJsiUKWPqqZUZSa/VGWOUqmVclz+3F7Skje97NLUHhtknsHwYSokgzm2m924mlVZRSoV3YLplan+m3ryvtSeOU7KbGRwV2p7Y5el9sPPPJrak5Vqcj8q3xJKxnSmGK0GbMw0Y2cKonewmf8BWYpBp6nSZy0JphrVZA2ZTe6xybK+Crc9DEv+SBtJQFLdGpBhJqSdVDmlzaTl6MJRahBRpZ9ljfEG4mdKYPg8qdXl+VPnulmdJWVWr/M+GtIuSlVdjK40xsDLx+unKnrDSOedUl9CGcl5NPBjO0dfoMqrqPP8ztCbSuurLX2i46wpCsa81fnD50Euo8xUlbBQ95mcgm36jPVSoWLQEppQIkBqXsKCBIjNy4+70aRLgfMUOTg4ODg4ODjAvRQ5ODg4ODg4OABYbvDGyGKiErsk/TzdZwVRCjQrdPg98jQDE157za080SoGeqsdeiY+3xGq04ordqZ2KI7AI7L7fGBBcriM05U63scQjC8flZxskrdlywq69GsHqQorz9JVfHiG91HfQpXGRtn23joVq3MaB5lbq28VKQWvn+2y//gzqX2nUASTpzW4HF1/+07T3dsLeMZDf+ls+kxFAqokyYuSZHCY7behj+OgdSLOK1boJ0225aY7UntgFSmtG1/zrtS+6ZW86OFv3JPaH/n8Z1N7XvzImmNs1aXXp/aP/+LP8rqrSJf2FxhctHjTG1L71O7HU/vEnlipEy0w8OC1Oy5J7fG9HMszdfKs1ooSTdpOg46FQWdK8oLCWiCI3c6h+KVDcYt7prP8KxJyMlPTRJJy4vhzadGhvVQvDV8p1GjI8VoXGsusYZ8PvPpHeG4N6ihu/4V9nCfH7/p7HlImrVq6jufZn6f6ce8052EQtgPECS2QCV7Je1Yqysoc9LowZrbXdKhlgNBsrFilKViqFEddFEqyqwELiV0R+kZpNyvzO6vmkWrJuTsF4wMWqXmVkpFGNn7n9jNC0WdyBzbjsVUPOMamhRqbaPD+ZyR48Hxd1vBmZ/oMF4HaNgYoJVEQQ3lWBqGsnRocVvtZVdPCbZeStmqIUjBUtWTmvoR2lX7ICQVVlG0k+ZzQZLJVQPNidlPaZqKfKoSSa4/pnORMKxU7B2PWvKMZii8TjFMpxPYWgs7VOBecp8jBwcHBwcHBAe6lyMHBwcHBwcEBwDLpMwM6cIuqGpDIT5U6FQ337Lk3tW9fIM2x6woGXKtMxAqT8MRuXuhKKpr8Iqm2HZI/LCjQNRqsZOCxzStIdQ1KEEj18A2uL0k5Xa9hle7h7VuGUruwhsfnIlJfteOH4+vXeGz/LqrsrKglvv45Kmn2HmDwyijkMVZd2zart7rQCKMIlYQKDTNeV1EkqAtbXqNHNzLA4cYFUpetVvzbkR3MOzZ6GfO8KRfQP7Q+tX1R3Q2vIP2ZSYsX8rd5cSnPSaTPWj9pslJB+5iniVZSPbX+FVSiTR3+JABgfpxKuI27rkntDUOHU/v0guT3k7p4mgOvi4qvV7A2QqMR06BW3dyeBpfsTJ8praQu6ihRYk1OsuzOr1A5ahvMO3bVJVSElZ/hMbNfZpDOfJ50ZOE2BnhszZFCPn33Q6ldPUol0ZbLeK3CVrb5Y3u+kdpzErgQqYpM55EEhetCNRj1u2twOaFcwnD5LvqlwFqbKo1yUjfPV8pCFDfCcc03uS7OCG07k1AMU1LnKZlTRaFmhowGaVSqWOgzyX+YYWp0jGUCT8pBci3NG+nnNKgjr9tM7mNGggpOCk02XmMfn6qSJhuf5biqN7TuSuX0Xn/mGaCvrT7Lab/xmEDX/kzEUZp5DYyYBE+uypM8CDqPVx0rXVL+wdd8Z0Jj5vzOQTqVytOTBtK2gdC6Sne11WoDongcGhA6XX4XKfUnz49ArymqvHYetOVMTecpcnBwcHBwcHCAeylycHBwcHBwcACw3NxnxmColLi5hN5pyitWM6CCa8+jVBIdff4nUvu6naQlchuPxb87zeCG+aPbaF9Cqq0krjwMCTdG1mRp0bjkmIF1A/IHtcUBFwlF9AKVNY0n4nxPZhWpwcK2G1J7dp65hw4ck130gwxil29Q/VaTYFdR2Fv6xVqLZqLmUGWIvi7rzv++ISrOrhwlXWiefzK1c8W4fNPLSEv1DzHYZRCyHYWZQCD3HYoqyR8YTu0bbro5tYdnDqT2kxJEtCF5gtSlG1m6Vxt5uuXHXvaq1F67+xEAwJHHX0jLqqcZMPDKdaR0D45T5TRpJailUAcNUUoqjdorNJsNHD+WKOSEPlM6zPc1/5QoTDKUqdyPF88xDZS2e44qsGOnSQO/9zWc39duurqSYbsAACAASURBVDG16499ObVP3fm3qb1++BdTe3aCirOp3VwzGpYKwpnSO1P7xAH2y6mTzHulSqkoyY7kZQJWdg4GaszZrv3kBzyfJznAvLPPdyFhweGbVcwpfdY5UN9Ck6qsyTr7aqw/7ssVEftyQOZ3UWxVExfzQkNLAETIdTIp4jQIYRf1ngYLtaqOzHGtaAmFN9uKz3lCVGbH65xfp9UWleJ8lWtrJGrQDM1te0+fGQB+0kuePDfVM5HzhA7KyTjVthUlWF+y7qhqKwjFDlT5J+dQhays+3VR6gWydunakBUW6vl5gabSWlJ5I5UoJFTi0ADXYk+2sZicrkG6lrE8ipSaE6qwfcwyutV5ihwcHBwcHBwc4F6KHBwcHBwcHBwALJM+yxV8jG2MXdolERk0JV9VPaTb07Z40BOPMn/SbVdTSbLihphKaz7Jcyw8R3qi36dLtbSVeY4gqohueZSWhEwMMpUDUPnSOkhlXOVh5i2zpZhSGrmddIE3SPrwCx/+j6l9YPxMaq9fS/psQgInSsws5PKkOibGx1/sLs4f1sIku/ZVGQDJ/RSJ63JI6KONovyImhKAK+mfFZdtS8tCT2gkoQS9SNy1Guiwj/2987bXpPY73vQe1mWGAQTzT5BK64NeSxViKrPg0PdXXJ7aG257IwBg+tCJtGxh6vnUXn8J86ft2kTK5jsnhf4U+s4Xysl2UX1dSAStAOPJGMsEVpNjjFEliaqsNKcRi71EtaJB9XIFqgMPecdSe0Ryao1cS3ochsEw5w6SIg++9tXUnjlNtdrMOBVDfTu4ToRbJlP7mw9/K7VPTXJeBUqvJ3ZO6p4Juqj3LHRjRu2lqjQ5pq2Ssi8hz9K5oCrfKFIFV2cqTZVdtsW5dGaeY7OUUBZFDcgqQRqVvjFCL60XxW9B6Gyl900gyi4olSEKZSUsJS8gSkKvS5DVaQnWejihzw5Jjst2GQAcXuB9TpdpBxKw0Ug7RnJ/FyP3mbUWrURRlpMxk1dVmOYBE4VakAnkSBSSHJsDAzy2JU0cijowklyUGgw0EtqpIe0py3uWJtO+7UYdZ3Kr8Zi+AsuH+mJ7sJ/nLpY0qKVcX5Wxcj4Nghnq0DLt+p0/nKfIwcHBwcHBwQHL9BStXDmEn/6ZJG2DZLI98jg32x46zi/Gpmwqmz7wYGofP8pNsyuviOPGlG6XzccT/KJsHGIcktzCwdQ2a3gOb4QbpI182WQD3Qj0C09iQoQTjEGEE3elZmW/ZHGXU468NvYuhAOs+zc+ydQUd91JT0OlIhuCa/wMKopHqODT+yX7DHsEK5sPO8dmyZWYrmPnqlWpHZx6IrV9nxsx114Ve8z6x/i7UFIJZLKxy5ebxtdYsW5bar9xA9O9jI3xK3V4ze2p/fY1jH0zKClHPP1S0lhCsqk2lEZeefltAID1V3w7Las+zHFdn6YH40bxGp2YfTi1D85yTmTSHUh8l17BwqKVxDrJeodoazb4zDGLzrQYmU3ZEk8l7/FL/K49d6d2S9zIr5WUOrkDTJVy+M5PpXZdUqWYlYxrtWI7Yx997tt/l9oP7ua8arboudKYUe06+xnvUGdPkcbjUs+SprtQz1/bs9Ss9y4VjzkP77d6k7Q3F2qs39HpqbP+DvEgWI/zq6VpmyK2x/o8vToDQ5IKpELvjMlko+elrKxzpp/XCgYYV+6MHH9U4i29kJxzb4Wbu1+YYjy8EzP0FparfP6EmXhIUhfb2UvaK0SR5UZmTz0fvPaAChzkGaOCAN2k3deOx1OQ8w2wTzyw/RqihAokpqCOm0wWe/VCasN11iBkyvNiq3dobIBr7Uji3eoTL1coG61V96MePtl/nfFg5eWi6SNmGf3qPEUODg4ODg4ODnAvRQ4ODg4ODg4OAJZJn9VrDbzwVBzHpVzmhuIVQkMUSnSTBnOkMIotbqSekpQeE0kcm+HVTPtQXEt6wk5L9u0XmAKg9QyptL5VEu9gJWPo+EL/GHG3BWXGObFTdLdWj3IDrR9yo7U/dhWvdQtpu2hoDADwxJe+lJZ96qv/yPMN8J629nOz9PP7T6b2bJ1doaHeG2FvKRdjDPx83CZeU2L6CMUwLBnOtwrtlF9g3YoDPGZoOKYyFia4ATcTg6jFMdMSekvDtKsv1hhSjidq3Fw9Jyk8dGNsdZa0Sll233VL3KzxmfoayTgrcPxY8dc2J7kBe2g105hcsnYstU/O85gahMq7CJm4gc6h7ZUqsF32Rtpu2xLbVKq4031x6OdEGTAzwz584IlHUnvd9Vem9lWrGXeocfB0age6HI2Sbvv2DOmSu/aSvpyraDAtoWsydY/rZrpsVF0Kg6Llmiqh3bMt2dR8odHe1JqhNeTvGl8nEwNIdwbITc4mKYyCkBvWA8l6X5eTl3Ncw+slru2hbJBe3Ufayx8itZ6X+a4NGBVJvTULXK/npPZHqpzvLzTY33sXYqps35lTadmJyanUrtSEMpNdutpnGfGB53U8ppdoU3lN6aDMRmTpXV+epy2JGdTKcGnxefqFa/P65M9C/S5Uec2q0FQted5YoUlNpO2j6XCkjr7Y0oT9RZ5nVDZSrx1k+/f1x8e0fKmLjIOm7pxWyl/6sKB0o2xMb9Nq/jK61XmKHBwcHBwcHBzgXoocHBwcHBwcHAAskz5bWKjj3m/uBwA0qqRC+kRtUrWi0pCYRc/OUsmzt0yX946ROObIzqtfn5a9931vT+3BK16W2p6kvbcvMB1D68y+1I4O05WKQFyLSp816aa1hm7d/NCG1DabmLqjbyfrEK4kBbD7038PAPjvn6WS5gVx6/avkLge6oUU92hDXPANUWqV+nqbGsL3DEb7YxpqXFKzmAKVfFesIzWGM4wdZcXt2pRx8MLXvgAAaMnoUpVZKLGJQokVEqoaUOmeLuqIkoYdErsgrl5VuqnKwstIYmjmkvrkW6RsbJ22gVANE0xvsXPtjtR+9hgp0mZDXNC53scpAgw80ynEvYbalzgjnZsho9BqZzDvIkBBILGgNCXMzAxVnA8co0po/WWcU3aQqr1ciXNqdBvpyCf2fjG1T89zXllRTRlxr2dpQO/s+8lQiZ3bQk+RaSOhhYLkD71K8wEIrdOlztn4ai9+vvYUqEi2+CMz7JspSfNwuMI5fXiM/XF0gPzM+tVU3PavoT08wFhDuTzXubkGzz8+T2rsxBT79bjEYzsxQXt8Nt7uMCfxiOpC/WXVd0Q2U7zG2LnIPgELmGTM5pS6g26XkJQbEretJnHgAr3NJP5bXtIi5YVGWiHjoyjXnBPaqSXP5zCQbRwNValK7DVptsF+/jYviro2NQYA/UWW9w8p3RX/v9nsHMOuT2j5YpFUq5+JeyXbOXz9bfx/7/zFZ85T5ODg4ODg4OAAuJciBwcHBwcHBwcAy6TPWq0Qx07GKjJVZuTLkhlXdrf7RnfOs/zxex9P7Rf6Y5fsQ49TkbX/NNVL77r5utS++qZXpPbAdrriw3nSGfYM3a7RHBVk6mP2JbQ81pIyK4ysTe2mpEQ4dZL03Df//q9S+xOfizOATwqFNCzByApluonHp6h4m6pqRmS6B4dXUC1327VMQfExST1xoRBEEaaSgJKhuJkLRSq7Nor7O5on5dhSd32TLu3WeDw2wkyUL6GxVI3SRfKj2ZKR57t7U7LRZ9zlEqCzKCq2IjpTDepS1+BpYWJqBm2dJYGU1yp0b/dt2MxrFjmuTYMUcRT1jmZRdKRzMs2gFIKiyzdSO82H/D0MO1OdeouqsDnT4hyYGCTlMXQZx7fXT8XZ/RIodfdTx1kVEXplAmMaDVinSq0kuF1Gcte57zM0S9cEAaLUaR9+Ebq1exDHzvyZKh2FNUnPoxRjQ/iYQNINzddF9SlpVPYVSNWU+kilRbJO5LoEyGwKPVKT9BuVBq9VF2ovCDWYX0JXdpm7Vm9UzKV0j+kmybzAaF/GhrIGyqVrOr6k5i1RggXShrkkYGGzpcpdUUjKuTWdyICowyLZxpHP89kzL4ExNWDkQIlzrb+LHWiqJUu7LFtm2rk49J0gEwBSthuEQlt7Qv1llgCaiNptt4xudZ4iBwcHBwcHBwe4lyIHBwcHBwcHBwDLpM+sBcK24kTcU2Ggvixx93l0CRZll7x6WJuJR8wW6Crf++znUvu/PseM2Jc//lxqX3EDVRG7djDY4/q1zNBd2ExqqiCBooKILttgmhTbQ9+4P7Xve4b5vZ55mBm9Tx8mzVcJYqrJCoVTaVB5MyW5oRpVuhI9I/XSXGOSH6iZ623yM2uBejtXVoZKoAu7Uue9DKzamNqeUTemumOTMs2sbLr5MYVCVNojohu/XCP1EnmkJYdXMvN6oMnoRLVgxfbyosCSuoXiGo4Ksft48wiVNF6VwSCPlOlSDsC6vCC06JlyZ8ositTF3StYUpWqmuqiMrMZ9sV2/EPbXZ3Jdq5qqC5RMfU69Qbn1+GAbdiqs3z2KOf+/iO056rsqwxlKnXPZLLXOiTHa9tn26JzML+s2ktUi50C/vUqb5YxvF5XeqebJrAzpdmehqogCjPtoZEB5Rxy7klZz0uBqhrZxt2CIWr/af6tiqRkbzY1YK0qx7zFRdn+kD9kx3jnZF0ZGvkisGcRgLaITNcFr0NA0Ph4pdJkLdU5kHRXIMEqI22HDLUt5UJBlSTQ8vCg0KELnJt5WQ+GJJO9r48nGRdNURm2ZDtDs0NCRt22YTUnnFBwmkutX2jaUl5arMDXmVqyJi1nx4LzFDk4ODg4ODg4wL0UOTg4ODg4ODgAWCZ9ZgzgJ66vSD2T4tVTN6kRX+3aTaQlin2kj8bWxrTFJdsZwG14SAOy0Q8Wegz49o2vMk/Zhw/xmmvX3Zrar77ulal99SBz/tzz9JdTe99zVHYdP0Mq5NgcXf3NqgSEFOQH4/sbFbeeCRnwT92ATc0J5NPFOJjn/TXrvOa+5/Z0vOaFg4HXpv3EjdmS/ENPHGSuoTXDpIxKFKjBk4CJbTWBKst8T/LbtOgeb7Y0740E2Yyo5KtJTiPN5VOsCn2XE3dtxPMP9DOHWd8KBpQb7KfKYvIUA1JWB+Pzr37zP0/Lgkf+OLUffojU6myN47oyz3o1xV3si3tfA0n2CtZy7plMAMbsMW1kAjl2iXSWuuCVufKUcxGqLZuYi6YoTBp19u2x01SMHjwsQS+FZkYm+J7WXQ7JUD2ZymMxbJc1K0NddAn6qTkBva4Ktd6iqxJNtzJIsX75tttM6Zvs6byzjgUWqfc035Qo0TyfcyBbR6Xl5ZwiJTQNWS9lG4LmJWyPVe1RrxvlmeVWpV56eBd+uUewFmgmtI4KrjzpLX1WLkq6J6aoZRMKUulNtVtd+KOCnLqQ03Wa5cpMqYrNZHqAda/XNY+lBuvl0Vof20FNGHaRi+X1+Z8TW7bjaO7O9nM2DM+/X52nyMHBwcHBwcEB7qXIwcHBwcHBwQHAS1CftT3K6qrOuEwl8FKpnzxLK6CSqTXP4ImDAzH1VJ7nOebEHbeiwHPkQTqlukC36/QMKZ/BNaTmrrxuV2qvO/Ngaj/64KOpvecUd9q3QnX9iUpJuMJAXOp9xbju2zesSssGCqTS9h89zN8JZTa2jq6/jatJ86wqMe/YxASpNOrgLhyMAQqJn7QlAcAiUZJMzJHWmFqgrTl21FsdJvSR5nYrFmm3QqqztE0L6y5J7e1rdqb2aklytjDDfpoHlYcrNnAsrRCl0ZoxquVyhvTZXEQa8PJV7Lczzx8CABw4QFq2coxjbGKSU6aurmmhW3LaLtHFddEbqKddXdV6VGeVjuniuqZ7W36nEz/zs7MDJwJgoiNkVYu+Khit5ifrRhF1VoVZ20EVJsdoMEZfFZ1dVGuZXGrCdZguwR57hfbVog5lZ/0r8wftK6EWk2N8VTOZzn2mgRGVZs1JeS4nOS6lj7PUjyhTteYZRk4C9Sl9liV+k1N3Dj6qtBsywSvl/oyOGR7eLW/ahYQFA95mNH5C8ajKqiDPEF1GAp18Sdvmu8w7VUTrVftkq0cxz37LTmWl/nnOugbPDDuvMZngoXLWusylNsWWodxl3Gi/RUKTNvWZ3JSWFLudUzNcxhx1niIHBwcHBwcHB7iXIgcHBwcHBwcHAMukzxSqWCkNkLIaXEl6wg9JZ0yemErtKCQVMn06ptX2PCOu6iHSSBsHGFTKSi61TZdsT+1LLyWdsnkDy7fsID2CHOtVFzfjwBDVSPWIdVwoSw43Ta0j/sz+JCBkuUzV2niFCqjxBdJFxRWsy/AqUj6rtlCVt17US2eOMkhkL2AM4OfaQbTEXd7lfbmLqCRDZbSVRupyVUWEKXGcbNt0RWqv3X5jak/O8HzHptkGUYvu4NkmKdWJw7RHh4dZr5F1qb1mFcfQmtLW1L7tOgb6fPb4fwIAfOFTf8NzBMzB5w9QHdkvKrdAFHXqLm7WWd4tz9sFhSGNoPSO76mbu3Pfehna6WzaIsvAdaaRlHbVdEw5oYT9PO2c1zlfVlY5J5RHJuWa0oByr8aedYySI7lMVEs5dxe1j7ZLhmVJftsrGs1AKIQu7d11SHWpUvt4DQyotGVGlaanU45DKChVIWbiNWr/KZWi1JuvFJcEMMzQXZ0CUnYOWKm0bIZ265L3LhPMEL1HIZfD5oSqD5QOk8CLRQ14K4OtIc/QUpHrp2k/wuUcofShnwk8yvK+osw1oZMz4WV1Dog2M5cJyts5V5tey5ff+kK3hUk/Z/pB57fY+UzQ1C40rY7p9Pl1/muu8xQ5ODg4ODg4OMC9FDk4ODg4ODg4AADMcly/xpgJAEcufHUcXgRbrbWrL+QJXV9+13DB+xJw/fldguvL7y+4/vz+wXn35bJeihwcHBwcHBwcvt/g6DMHBwcHBwcHB7iXIgcHBwcHBwcHAEt4KTLG/FtjzLPGmKeMMU8aY17RywoZY+41xtx4rnJjzJeNMSvO/nXXc77NGPM7if0OY8yucxz7G8aYn0nsDxpjTiT3/aQx5s1y3L82xrxgjNlnjPnhpGybMcYmxz5jjPlCu57GmA3GmH9YQl2rxpgPG2Peb4z5kJSn9TbG3GWMGe1+lsz5ft0Y85wx5hNLOb7XMMa8xhhz21L+Zoz5qDHmXRevdp1xrjFjjPln7fFyjt9fr2PnYmLxtXUu9Pi6xhjzLWPMm6TsJ4wxdxpjHjzXb5Njy13KvyfGxPcbknXrD+Xfv2WM+WCPr/lvenn+HxToXDHGvNkYs98Ys/Vcv7nA1//IuZ6pyTEd523yzHxP72p3/jjnS5Ex5lYAbwFwg7X2WgCvB3DsXL+5GLDWvtlaO/viR6bHf95a+/vJP98BoNsDLgfg5wD8rRT/sbX2+uS/LyfH7QLwUwCuAvAjAP7cmDRIRpQcezWAaQC/mtThpLV2KYt5DsCdHcq13h8H8CtLOBeS495grX3vEo/vNV4DoONL0Yv87buJrmPGWvsX1tqPvcjvrwdwXi9FJhOM4yUhc+1Fc6FnsPFmxX8G4I+MMSVjzCCA3wPwq9ba78U+/kFHA8A7jTGrXvTIC4eOL0XJC7VjMc4TxpgfAvCnAN5krb0oG7qNMb619hestXuWeYptAP7pvBQBWA9g0lrbAABr7aS19iQAGGMOG2P+wBjztDHmEWPMpUn5amPMp40xjyb/3Z6UDxhj/jo59gljzNuT8j5jzCcTb8ZnAfR1rgqRXHtV8pa5N3kL3W+M+YQx5vXGmAeMMc8bY25Ojn+/MeZDiRfibQD+n8Sbs2PRqV8H4DvW2mDxNRfh7QA+aa1tWGsPAXgBwM2L6ngJgNcCuD65/teMMeWk7r+ZXL9mjJk2xjxm6B3zAdwK4P8E8B5jzD3GmGMA3t2uN4CnAPwvi673LxPv1DPGmN9Iyv4CwCUAvmKM+c1Fx99kjPlMYr89qUsheYAdTMp/MenD3Umf9htjhowxh0yS2MwYM6z/lvO/1Rjz7aSv7zLGrDXGbEP8oGzf/6vk+G5/u8MY86Ax5qB+aRhjfjup21PGmP/QqZOS9v5PSf0fNsasTcq7jdE/Mcb8+8T+YWPM/S82ZkzsTfytxL7XGPN/J2N8vzHmVcaYQtKX705+/+5zzIX3G2M+b4y5B8Ddxpj1SR3ansdXJce90RjzkDHmO8aYT5n4haPdpw8m9/uIMWakw7VTD2Qyf+5J2vBuY8yWpPyjxpg/7dTu5wNr7TMAvgDgfwfw7wF8zFp7wGS/bM/ZjybGh0zskb0LwJrFxzhcEAQA/hLAby7+Q7dxsuiYc43pz5jYQ/i8MeYPkvLfB9CXjMtPJNfYZ4z5GIBnAPw7Y8x/kfP/ojHmj3t07//kYYy5A8CHAbzFWnsgKTvfZ/H7jTH/mKxjzxtj/o8u1yobY/7QGLMbwK0my+L8fLL2PWJixuND8tNOa/nvA3hVMg7OGnvfFVhru/4HYBDAkwD2A/hzAK+Wvx0G8G8T+2cAfDGx/xbAKxN7C4DnEvv3ALwvsVck5xwA8C8B/HVSfi3iyXljh7rc2y5Prr0K8VtmAOAaxC94jwP4a8RhLN8O4HPJ8e8H8KHE/iiAd3W53/8A4Nfk3x9MrvVUct7RpPxD7XtJ/v1XAN6V1CcEcAXi/K1fQ+xJen9ynj0AVgOoAbg/+e3Hkt/cmNyTBfDW5DezAP4vACUAZQC/LNd8HsDKxH45gKeT9hwE8CyAl2lbdbjXHICDif3/AngUwO0AXg3g75LylXL8f2y3DYD/DuAdif1LAP6ww/lHQXXjL7SPSdr0t7q0f+ZvSV99KunbXQBeSMrfiHgBN8nfvgjgjg7nswDemth/AOB3X2SM9idt91oA+wDsWMKYSeuMeIy27/PNAO5aPP5eZC68H8BxAGPJ3/4VOMd8AEPJGLkfwEBS3n7hKAA4COCmpHw46ePF107/jfiF5WcT++fA+dKx3ZfzX3Jf+xCPz2JSVn6xfpRj3gng68n9b0A8Jzr2hftv+f8hXl+GEa8XIwB+C8AHzzVOFv3+XGP6YHLOEmJJ+mbt48Tehjgk8S3JvwcBHACQT/79IIBrvtvt9L34H4AWYlbi2kXl5/ssfj+AUwBWInZOPIPOz2IL4Cfl3/cifn5tSMbPGIA8gG8i+9zttJa/Bsm7w/fKf+f0FFlry4gfuL8EYALA/zTGvF8O+Tv5/62J/XoAHzKxR+PzAIaTL9k3AvidpPzeZIJsAXAHgL9JrvcU4heQ88Eha+3TNs4z8SyAu23c2k8jnmjng/WI77ON/wZgB2IK4hSAP+z0o0XwAOwGsBXxPX49KX8IMbU2gXhQ/VlSfi+Adl6RNyJ+Qfpi8u/HAay31tYRPww03sI44kEIAK8E8FlrbSXps88AeBXOARt7ww4YY65E7OX6I8R98SrEgxkArjbGfNMY8zSA9yKmCwHgIwA+kNgfQPyStBibAHw1+e1vy2/PF5+z1kY2ds+uTcremPz3BIDvANgJ4LIOv20i25bbErvjGLXWVgH8IuI++5BNvrjOE5/pcL3F6DYXAODr1trpxH4UwAdMvLfjGmvtAoBbEC8qDyS//1nEY+0KAKestY8CgLV23r64x/NWkCr+OOJx1Eandj9vWGsrAP4ngI/bxOMsWEo/3oH4JT20sZf6nuXWxeHcsNbOI/5I+/VFfzrXOGnjXGP6bmvtXLKO7UE8XjvhiLX24aQuZcR9/RZjzE7EL0dPL+vGvv/RQvzS+POLys/3WQzE68+UtbaGeC3r1NchgE93KL8ZwH3W2mlrbQvxS5DigqwpvcaL7luw1oaIG+7e5AH3s4jf+oBsypi27SF+26/reYwxBsCPW2v3LSpfVsUFutBG8u8I55/brYZ4gAAArLVn2rYx5sPgA/YEgM3yu01JGRC3w/2IB+G7Ee8pmkf8gFa0/63ZlN4EoGmttUm7NOUeLOKvZRhjNgN4GeKX1D9d6s0ZY76KeDA+Zq39haSeb0I8qe5C3K8+4pcYJP9+h7V2d/Iy/BoAsNY+kLi7XwPAtzFNshh/BuCPrLWfT4774FLruQjav0b+/5+ttf/fi/y2lbwgA/FEbrdlxzGa4BrEL6kbOvztfOqr11uMbnPhFQAq7X9ba+9P3OI/CuCjxpg/AjCDeOFaTJ9egwuLTu2+XETIjnM971L60eHi4b8gfkHt9KFzLpxrTOtYOte8qCz690cQ7zvau4z6/CAhAvCTiCn3f2Ot/b2k/Hyfxa/A2WngOgUyrCfvBeeLC7mm9AwvttH6CmOMfrldj2xEznfL/x9K7K8B+DU5x/WJ+VUAv5Z0CIwxL0vK70ey0coYczViCq2XWEBMQ3TCcwAubf/DGLNe/vZjiN2JQPzC81PGmKIxZjvir9tHkr/Z5NifAvBlxBTI4nauI968C8QP35WJfS06PzyAmCZs77c6jvjheK219i8Qe3beYeI9PwPJ9b+5+ATW2h+28SbwX0iKvgngNwA8lHiwViL2OLTvcwjAKRPvF1q8UftjiL8euy1WI+CL4s9K+bna/1x/U3wVwM8Z7qXZaIw5n70mHceoiRUb/wrxC+ebDJWWS61XNyz+fbe5kEFSnzPW2g8jfkDcAOBhALcb7uEbMMZcjpiiWm+MuSkpHzLxZu1z1f1BxOMUiPv3rDHTYyylH+9HvCfKT+bjay9yHX+gkHgp/x5Zr8NSxsmSxvQitMyivYiL6vJtxB+f7wFZCYcOSLzcPwrgvcaYdt+d77MYAN5gjBkzxvQhfkY9cB7VeBTAq40xo8na8+NL+M1LXVsvOF5so/UggP9hjNljjHkKsdv+g/L30aT8X4Ab9H4dwI0m3pS3B/HmWSDeG5MH8JQx5tnk30BMUQ0aY55DvCn08Zd4Ty+GTwL47WSD2eKN1l9B7K5vo72R/CnEi/FvAoC19lnEC8cexEqxX9U354QyeAvi/RBnEFMeigUAK5P2eSdiPngzYhqhG04ifhF7AvHG34fb9Ii19juIvTqPAPg25/9y6AAAIABJREFUgI9Ya891rja+jdhzdH/y76cAPC3elX+XHPMA4q81xScQ7xvqtlh9EMCnjDGPA5iU8i8A+DGzaKP1Ev6Wwlr7NcQvZA8l3st/wPlNrLPGaLJA/BXi/UEnET8UPmKMKeHcY2Yp+AaAXcl9vRvd58JivAbA7qTP3w3gT5KX1/cD+LtkXD4EYKe1tpkc82cm3gD5dcRez8XXVvwaYnruKQA/jXgeXzQssR8/i3j/3B7EL+IPwaHX+EPEe9faWMo4WeqYVvxlcvy5woX8PYAHrLUzS6r5DzCSF9ofAfC7xpi34fyfxUD8DPk04mfBp621j53H9U8g3q/0COJnxmEAcy/ys6cAhCYWh3xPbLRedpoPY8xhxJuwJl/s2H9KMLEC7n+z1j7fw2v4iDnyevKQbVNX+6y1n1zC7/8EwOettXf3qo5LqMO7ALzdWvvT3606ODg4fH/DGPNFxGFRvmtr3Q8Kki0SN1pr//lLOMegtbaceIo+i1hE9dkLVceLgQsVC+X7Cb+DeMN1z16KEKucvpG4jg2AX7HWfuU8fv/Md/mF6M8Q70X6rgQkdHBw+P6GiYPePgJgt3sh+ieFDxpjXo/YS/01AJ/7LtfnvOESwjo4ODg4ODg4wOU+c3BwcHBwcHAA4F6KHBwcHBwcHBwAuJciBwcHBwcHBwcA7qXIwcHBwcHBwQHAMtVnpVLJDg6dOyxMK2ildtRiMGdPAmT6Es3a+PH7WSD7vm3EOIZWfhfq3vCI/9AQmRoo21r+Q8+TidUpx2feFLtE3I46bVDPFHW5ZjfI+bpUC/VGc9Jaq6k+XjLyuZwtFQvxPzxeLQw7BSvPVBO+Ly2l9W+HbNL+laTXVu4qCjsHRi3mOTRzef622WDmimagv9WYl3Jd7U1pTI2k7old8GO7WOD1w5DXjDL3KefweB3fh9g8vpAvpPbhEzMXvC8BYOWqVXbLljiLgu8tIWiszB/tiyDQdo7nbyh/t13Gq++z3Tzp81azltpT01Op3WhqJhJpOKPtyXKdjtLkmT408gfPi3/r5xgjMJfj+TQTSqteZb3qXLOCQNchnGW3Wi2EYXjBI/QW8jlbKhbjf2TWs87zsXPw4ewa0p572WWtc9W7i3A6X7/7KTuffwmHwOg6mlzMLjoitYyuuV2QGbednwXVWq0nc9P3fZvL5ZJq6LpQFJvzR++n6+BKjun2d2N4nVKBdqvOcW9Lg6m9Zi2zb0S1hdSenOScDSPOL5NZY7SvulVYcXZ/GtP5fJ1+t/iITpdcKJdRb9TPa24u66VocGgIb337O+NKSaNbw8XjzESaIQMLZ46n9oBE+h4tcqHyBwYAAFOyADUbjE7eDPmSVZZ1NKxx8SpJE/myMAYRy1v6EBDb08EjD3tfFlB9T6jLQ6M9AjwZLKGlHUEX/s4LShjyvgN5GczJw2Hv/oMaTfyCoFQs4OVXxUG8bTHNcIK5Cts+0oeCvHsMDg6wPOBDLwziaP2ePMy8IideIC8T5VlOPH1J3rZxNLVXr+5P7SNHODlPTszymhHHlWf48uH7XHCMx/rog3FIFqL1I/HxOzZzTaxUxlO7Kg/LZovjt3+wL7WHh3nukSEev3XDxtT+mX/9Dxe8LwFgy5atuO9bDwMABvpkfnVbFqqsX3UhTV6P8UnO35On48DkMwuMn9cI2N6hjNGhYQakHiqw344fYiaYv/kkg6AfPMZzWm84tY08KIYHV6R2qcgb6eNwRUnWklyBv+0fjsfR6CpmbRkbG0ntqMUwa8f2fCe1j+4/mtpnZtguLXlxayULwrFjPPZColQs4qbrk5SB8vDJfHDKC7uNOj+J9Lllkznm5/Thy3uKZO2JZE2Closdip15Yc18dOjLrn5IyMMt8wJw9kstEL98Lr6m57PfjdiZTyQ5t7ZX5mNHzvnYE0/1ZG7mcjlsWBePw0CuvXHj9tTevJbrTkn6yIO229kfAL7Rjz/+Pedzfl+1hePmxH6m+IyuZHqzX/nN30rt2u5vpPZff+TjqT3d5MQrFbjW6gtsKzMUpf+hz9/4IyuUh4qRe/a9zq8nRh6cnthRxhER4zNf/ceO5zgXHH3m4ODg4ODg4ICXELyx0zdJJB6AUN9WxQtTFA/OrLwhlufiL8a6uGOM0CNKBeTEIxOJF6gRdT4++8Gj3iGWF8SrURC6Jp+Xryh1Fcs9takmKyfMy30aFKS8cxtVW6x7s0qvS7MLvXShYK1FM6EwanXmYwy7uD9zcl8lXz1z/ErrWzkGAKhK3zT5kYK85bCrSBT4lvRT3fCYE9OkNVCkF2BkgJ6ISpX1qjY700CeUFmQL9CmfGHM1OKK7j9BT8n6MV5z/QDt2SpvqipezXyLx2wZofdjfR/r2ytEEVCrx/efl+ldKsm4k9TElTo9PuUqPW+1WlXsuGPm5qQ/LT1jhX7eb6XK81Vm+DV66hS9KUpZQeas1e97mWwtWQf65FqRJ17liH0RSp/bhfg8vifeo7x40CRPc7XGeVeR/lSPZ9hUH0S7fXuT29Jai2biHYHJLD6ZY1jcrfzsb1+lftVjkj2G7a5rn67zWfpKvvg9/Wo/2zsAAIEszBkqVOamlbo1kzVS78cH+73VZJ+Fcv958SD5Yge64HRNOXlh0e4j9YKoR063LSjzog8rG2nbJp4i+buf8YzJc6XJudmQc6wRhqBP5trYTqZEu3Lnt1L76QOyZmd2MHTewqBtG3VganQs6hKta0Mk/hvThbPNUsLe2QcsEc5T5ODg4ODg4OAA91Lk4ODg4ODg4ABgmfSZtRZh4q5W1Y26LK24eyNx7TXkkmVRnrQ3LeqG0JyvKhFRrYk7NiebotWrmxO3WVEpM3HxNbzOLr5CgS7WUkkVVrqBjcWNZFeZuq/VPaoMWF2UeJG6p8Vt6UHbFD1F7KKPr22lb3QzHzL0p7RlkxRLUWiNfCFWJoaiFGu1SE0gpJtb20zVTRWhKRpCZfTJiB0dIYWTk/4LQqEfpV1VINSUsWd093iyQd7IBsLVslFwqJ/XhMfr9AuVs2UdE4yvHuGG8dmJefQaQRhgciZ2b+eGSN01pkT9tUCadMFyPOZlXjVrbJ9asgG7UuWm+KZQGE2hMNrUHQA050mfTc9yQ7VuyMy63HWTr44dnr8RsC/KDW6A9vPaRxQAtDegtqocQ+Xp6dS2ln2ysCAcb4797Mv6kRfBh/XiMWK6KFRfKiyEetK1VdtMBVS2MwWkIo5UfRZ1VlSqQAUdNq4CWToss6Ha67wW226bYbtta/C0vnrdqH1CFnqy0VcvGnS+v6AughBZb/L5i5MGtF1DL6P0lboqlZahhnTTNZE+zzJya6U02T57j0pf5Sle2HbJ1tResWYljwm4pr/8FTek9nh+f2pXTnEutcqcY9UG27bVhZlsVzMze5QazCgE5FmpdLAOhUx72bPPvUQ4T5GDg4ODg4ODA9xLkYODg4ODg4MDgGXSZ8bQ/ecJ3xWJKkzfthriFm+IDEldWwO52FU3UGSVWuIbq4sL3cvEqeA5gqaoxsRnNyTn6Rf7jNSgLIEVyjVxkUsT6a5+jRtRyiXXVfexxClqCgdWE1ei0oAlicsSiTyoEXVWhlwoWADNxI8tYW2wYliUUtLGOVF49Et5GXRvnhyPqZKGuMSV3jLaf+LzzHhLhd7ypAlaAa8fCGU3OsJgojlp7ynpy4r0cdRkG9c05FTS3yJSwZkFXrMoARhXD5CmWSusWv+IxFWaoqJrXBRtvUIYhJiZStp/llSBlX6bnmedmqoS1VkrVNlsOaaYyjWqTgJxYedDNpawWPCEwvCFFlCaQ5mQQINDGqGThV5oCP2saho0JQaVYfv3t4PlCYWt8ZiCFqlEWIlvJfSZket7eZVJxffdK/oMsEBy7azgS9W3GkNM55WsxZltCMn/NZhqpBKizvfidfl8VmouE4g10yZyLd0PoKoj00VdpFslkkOMBo3NtIXeh2xB0CCvGhNH1F2ZQLQ9ggFVYhkKUo7JxlvtQplmaMr2MdIP0iY5UVqu2MQ4abuuuCS1X33D1anty/NZ471ddcsbU3vTDYxrtCDrW3mO8+rUvsOp/eShPSw/eCK1q42kLTReH7ogE6VR2iXzzNXD2/84//0nzlPk4ODg4ODg4AD3UuTg4ODg4ODgAGDZ9JlBqe2W06CK4g4dFJdtWaRaFY3iJ+kjWolbvBZocC+l2oRqyqn7sHMwRKVoVJWhwRjzUt+8zwBWDXED18t01ysFNCzqJD9pi5pQfIEG4RJakYQPYDSMuYTCN5k8Wj1+bzVArhhfe3SYbbB9M+mofI7ttDAh91gX1ZHUs1GJyzUNCCQNSKNCOqJPcmX50k+hqERyGRc561KRvikOSvBICfBYEglHS5RWLRmH6vWvJg5cTfuSl1QhYUTFnVkpNE2J15+vkHqqVEnP1DRqYo8QhgEWpuPUFSekfWpCF9Ylx5eqPfNyz80K3eIL5TjNybz0iTUSDLGfY8UU2FeDSj03VQEqgevkmKAz+4GWrAOtQPKpyQ8yKkZJoNimd6IMBUe7Kn0VCL0ayjiD5nOTdCJp+ooOwREvBAyoolWaILCLj2rXR1VH3aLaxT+OugTNzOSJzORY60JDCAUVZM6p+am4tukWBF9+m9W8ZQglsePzRKGMgUhTnmiQQFErybNAgxwqdR9I6pRewfMM+pPtIXqPpQLXXeNlOKAUGjwz8zxrHyRjdHgTlWUvv+WW1H7ljstTeyySXGb33pPac//jb1jfSNYrCeaqMSUHRKndf9llqb1l56tT+8ZbqVw78Pzzqf2dpx8AALzw7Om0bFZUx5mAjZl8ljK2uqb56JQnb2lwniIHBwcHBwcHB7iXIgcHBwcHBwcHAMukzzxjUErcgFZon6bQYZ5mz1WKRGQ9odBNbVqk2RS6I6P8osu7pEoFVSeI+39FkfVaV6SSZFQyaOfEY7ogwaxOLFBl0xDaAUJ3SXot+F6YlGkOGw3GyHsqCJXYEjdoQ5Q06oaOurmtLxAMGOhKBHAo5Nk4Q9KWuVG25eSsKDxCGQdhrH4qi2rLFzrVE6ptUNrASobkulAjmWCagwxIqG5/zT2Wk0BsIyO07RzPWRZVnxF3fFtB05Ts8TMhKZay5KUrV6m2mKlzvG8epq354cxF+AYJgwCzMzHdNS4546YrtG2DfZsXStGGpPrmFxh4sZWUN1Vdmhvj7yLSiIEvuaoCXnO+wraXOIqIJC9gIOf31RWutGommqlkSBfap9XgXJqfj8dis8ogjZ7MzYbQAqpsq2tgWblikGE3kqCnPZujJqW+lD7J5D7LFCtl1vFwnkjHf+Ysncdo1IWy0C0FxVxn+i6Xk8CuooayVulkoW6Fao2s5J5MaNdMrqycUnZCjwd6f4IuVKfmsuwVosiiXI/rqPnjvJI+B3m8zfSzqjfl+ZdQ+5dvvzkt++H33JHalzX3pXb1E3+c2uGeZ1M7f5JzvZiZ4/J6oPkKJeGZUlzhI4Op3Sx+nsfsvDK1r3zD+1J71/t+HgDw7a98JS2778FnUntKAsHqvFM9drY35f0jaTsXvNHBwcHBwcHBYZlwL0UODg4ODg4ODlhu7jMwV43vadAo2vOmMw02kBc3bI677vvysZ0TFdiCOL8WypO8TiRB2yxdj33iPrtkhDlcLh1l0Kq5MwweFUo+rsFB1mtGXIihUl8SKTKjI0qoHnVxqzvf03xhqn4QNyi6tGMrkxuqF7BpHqQ+uStfOIOhlczlpWoPVEmlaaDAscG4Pcy8BMkzzAHm5VXBI8E/hcYKJPimBoUzEftgYIDXt+K6b4hKaqREeiY/QjWcp3SAz/N4Cb1ZXSBlpnnpqkJtalDBWcMxmWvwmgUNkofeBuIEYvXZXEIZVSpsh4oEVisIJVEvM3fR9OzJ1J6pMFdZlETP1ICGJYn0aULSIzZkO9Tmee55yX02ITnGmkK7tkQpGImLPqOCkkmmSiIViy002HdVtHMgqdKpi3pFc3FJueZ0DKz+tl2n3s1Rk9yj5gZTWqUbrZbzlHZS6iW2/S4BJ22kgWt1QRNq0xPFUZ7U6YAoMAf6WT4kOfiMUDIVUUFWauRUF0Q1WZXyCPWkXrL6Wp4vL4FVm6pyzpKMtOSevJ4F4FyE9LkgaknpxAxNmbEJT7agXL4zDqT4vrdS7TV28HOpXfnoR1N78AAVpX4m3aM8qwfZb/WBER4j8weh0u/sn7wobQsSINZOHEnt2rNP855+8kcBALe99e1p2YDkLbz7vm+n9omy5IfTyW50Pirl3g6Sef5wniIHBwcHBwcHB7iXIgcHBwcHBwcHAMulzyKLWiN2YebF9WaFJoqCzoEM8+riFWWOn7jNSn2kWYy4FUNxyzdEyQIJiDcqVMnKIQaUa83zt+EMaYR+IwECB9T1y/NYaA4k2XUvSrNU6CC+Ol+CreX1PqVc3YCZtpD2Kiyrh5YOzxj0J+7YUQlSaBfo2j7SJK3SEgqlGmhOJfb3FRtXAwC2bJUcYPO872dPUe1QaWmwTlX8SJBNCdbZN0rV08gKKgbrdaGHJCBfUVR9/dKYtl/GZB/HSiNRROZEvVIRVZIn6sG1Q2yvFZK1Z7asCixeZ80KqjN6hSiKUK3FbVETxVUkLu9cnn24UKbScnqK9oLcs0moyVJJqCtDV/lUnW1crbAfpqZJn83OsU8WJHhnrdmZGguEstTciSoe8n2lqEXdKN71XDLfNACpBuZU2sTLchS8ToZmkUMSUsN0Swz2EmEMYBLVZs5TOk/qkKl/55xyWQrBJsfKOTKB8djWgwWhqkVBNuAPyDGyPgYSLHSCfV87fkquLtcSGmjdCOf1Fas287eyJlSi+Jyn6gw8ePAMx7gvkmC/S1v4Ok6k34zXe/rMguyZKuiiTABCIlMjGevrr31Far/7J98AABi99y/SssbffSy1h6flWSnbFiprr0jt8BaqwwZvpYqtsIPXgeQkRUQq3EzvTu3qg4+ldusR5jvr238gtfsnD/L4j/w3AEBt6lBadv37f53VLXE8ffGrD6f2mSqfz0W5p8GibFtIFMuq9l4qnKfIwcHBwcHBwQEvYaN1mGzWDDWbuQT+yckXekG8KrqRz5cs40hi11iN69IlfbBmjB6WzXWbhrkxrE82tQY1btL2TSZISopWg16jFcP0QDQbfNMMQ34F+7ppNqloKCcMNWO1bgbTTZKyoVt3iurXjDn/F93zhEnjL01KuvjVJfZN3yA9KWFDYpRY9vGIZKxvJF6JZyf5hbjQEi+JtHupj96kkmw2L8h30uoV9B6uHmHfrJR+apR4/Un5MqyJJ2pe4tdYw6E/K3Gp2ukgWjLGSuJhGpDxO1hk3eel/ybmOMYCKa/b3qf5iGyIaj324szKRmcVDLRq9PJMyIbIdgyV+HiOzeGBeKPsihJFEEGLbTk1z/k1LR6h6Vna87J5NpTNsYFM5lzG6yHzTjdAZzy0nTOe52Qzr594fXPiRZZMJJnYZjpnM2kqMul4zvbMmJ5t0jVc93Qzrngx1cFhJfaQp6lJdJlpbzzXbPGWc2SgyHm/doDzblA3vosIwdbGee46x32feHiGTGdPSCgb/s0M52D1NNcNb5jrxppNsTcpt2prWjbpceyVT9P2JF0EMqlFWOyLBzrQXD+9hEldRWmR1fQouqE+R3vttvWp/YH3MnXH6mc+DiDrHeqf5r03RyT43I0/lJqld/yL1C7etpPHSBobjcuXhbohr0/Noevfk9rhW7mhuvFFbvyufeMLvNTzcT+3Pvu1tKxiV6f2VR/4ZVbFo0fygQfphZJQRmjIxv2FmXjt07myVDhPkYODg4ODg4MD3EuRg4ODg4ODgwOAl0KfRWfH5imIK2vLCGPbbBuQODcB3e6T4sZvO7k0pk9L3OOa9gHibtzYT5f+Zf10vQVz3AxWlXQD6y7jBrNmjdc6Mn00tQeFRgiERppoysZsCXvejhsixBxaocYaYnlOKTOrIep1syPh93j/n7VAK9m5eXyWfXNmgX2zkqwhPKGDNJZQXdqjktBwkWxSV0pj7SqWrx4Waqyfmy0HZaNvScZMY5wu8qZs4FQeYaXE0PFKpLimDW1NBTJV5sZva+M+7l8t9RoiTbeizF4+Ns/xcHCSmz9rdbaFZlsvyzjsFaIoQjWJFzI3T0oiQw4IFdIQAUNLsmK3dLN/Mgj7ZCzOlDkoKgu0p+c4bmYXOtOIGofLl42SfUXOZU0fUZX4J5q+IcowJEIPqiAi2QRuJLWMn9f0NKxjXeZ3IAtbXz8pnHyONFwuSWXUK/rMwqYb5DNxiqLOVE8k64kmW8/Lhub25mIVjYxIGp01/aTKUeF9zcrG6bAhm5tlA3v/CH87uIZr8YpVnEvKvExNkLpdmKJdL3PtttOk6vL1uA4jl25Py27fuiW1n5OxceQUaT1fKVoZV1HU+7hhi5H2Y5eN1kqf5UrcDvKaW9+Y2hsPcHNz8Od/CQAYnJE0KSv5zPLf93OpXXzLB1LbrN/ASp33JnONEyR0m4wjfwc3bPf/0tWpHd7B8vk/+c8AgIHHuNHafI70Wq1EmnT7225K7b0PfSO1v/Ls6dQ+Nn4mtctJfKsFWTuWCucpcnBwcHBwcHCAeylycHBwcHBwcACwTPoM1tKFK+5QjS0jjAtaokqIJEN3QxQCbRGZqhOaqpiR6wyKsmJMlVFChy1M0W02tmVtaq+9nu67mUm6y8PH6bIdlBg9jRG6BJsi42gJHUB3tqbzyEjnUktZQCuUk1H6TONqLCvP73nAGJhEiaUh5psR23hyVhSDnv5UUmuIO95LpFuFIsfDij6eb+MQ6YjtI0wBEEpW94UTx1K7JelY8qIay8t4U0KhPsPx5ud5zGg/3fhjw7xucyXdtNOt2I1fXEvKzBfKpNJin43LOGk2hLKJNMVCZoL0HM1mE8ePHQYAVGukxiLNNC/K0FDrLbGMQqGuG424PcuiKJpbUJUZ6ZSFqtBbYLvpoNbYIXmhVfs0zkhe1ZhCzVZEWSMxoKS6GQoiSuJOCVMPv8RrGklTUK2Qwmk0JNaUULl9Qi/5uXbW9t5lWG83W4ZG9zurppSizvkyBkNVmsUnLIqaZ7TAuVAQyrMi6tHhAvumX+IIrd5C+mrrNdekdnED19xQ5H6Zr3CJUVUfJz134ElmcD/zwgupHVRj6nx2H+PerA15z9vXch6fKXMs1+ck5USGc6Vpe73OxhdJU65kYiQZWb2kGqu2bkvtm1+2iYf8BWMS9Y3HbRgUSJmZ11IFVvqx/5UnXM3UVxfudmXsq4pO6HcjKl3/urem9vDPx8dUTv9uWtZ/VGLY3ffp1C697obUvm4z2+IzdzM2kpV1d0WihvaXEX/KeYocHBwcHBwcHOBeihwcHBwcHBwcACyTPjOQgITiFhdPPI7+/+y9Z7glWXUluE7E9ffZfO+lt5VlsrK8p6CAMngQFAIkISHkpnvULdQCTas/9UjdzUgaDa3ukUYapJ4RQgIkEAiEKXxB4YvyLiursrLSe/Py+etNnPkRcWOtyLw3zcu8VYzqrO/LL/eLG+bEcRGx11l7T3IleGWaSoCcuJqHinRFZzIduoLuLg3NPygu7/VCgxQlANjsCVIu6RwLMyrutsLa9SxXhrTM4D6qz9qHt8d2NuA+WXHvl4WaMJFLNCu0QDMRFE7d690DmWnYdyTCvvfPNQ+EAehy0X01ZKW+0j6+BsASV3hbKJmc1I0XUVxLR9lOqwfprh/3qTKqHWE/qZ+gskwDBa67nG75Zasuju10ljScBumamaHbf/+e52L7xDQVYs1jbNeBVXS7e9nQxXzkuKio6jxudoo0a0mCQXqaCkK4Dquu8T5mU4+vZy0aUQqeWlnUnUI1tUV91qGXAKAptFpDaJlOChUjY21a0uXMLchxoq7U7Oy+UDsZCbhakH6j9JkVrnGgyL6jaYXmy7xuVfiztubBiPpxo0EKbF5SA3lCn7WEMrNyH9Uqy+Jlugdo7T9ElST0mZUxGAid3RLKzJNj09Gsk09zfNkG7ekZ9nUDjpHhJaSTV112RWyvu5LB+7JLSc+UVeVlWK9sSSCVk1QqMgY35hmocCBHGu7ItvsBAJXZg/G2Q3uF4ltPCmmdpA05IGl/tB+2AqW8+zvPApGaMOozCbWkBEL1PFJNN1/PII2FXaSJ2g8xMKIX9fvq1W+MtxXf8xu85ijrZDai1QFgzz7OtRlRDW7cxGdlPsOyJLlGtufs/udj+3vf/HpsL8xyacHAWirHbn7Dq2J75Q23h2d+M9OJVD/OQI6ZA6RJy1/5SmxP3M1z3PKdb8b2dw5ynM5XwjpdTLs6T5GDg4ODg4ODA9xLkYODg4ODg4MDgMWqz4yBF+VFURd5Ihhhoyk23YMpocR8ccN1slkrDZEXCmpYMtdfosEQF+jiLVvSBaPrGeBrcD2zAO84vi+2i2m6BydW0307M0n3rClRWVNIU6VRMyxPEGWIz0rZNUBdIG7anKgOgjTz0tQ0945I1PrtorfWIoiyW/ui4VJ1RFGC3eUl03tV1EqDcu9+pAgZEwpsRZ6O85oEVquKgmvFWrbZZVcw4NcVr7iN11l5Ea8j9JkRN+nCHM+/4tknYnvrww/F9tFdDBhWn6TiYcW60H2cKYijX9R3VoKF1mqq1hOKUZosEeyy9QLQZ+026qVwHNSqHBtVSRLkgfeQVrral8CpTdJKpWgMzLfYxlMVUZGKAki91Zp53JM8Sinp9ynJtdQUWsuXPHiBVKgvOczySjkLxVWXeaUVUWnqRNcy+hLYMK10gVCF+aLkVBzgPn3XK1ky6Z7kQ1S1m0mozNjGibJpTrSIzh2SMW0lZ1S+yLYxhrRK3XAOP1LaG9uVSWnLFmmv6RLn4vIcx9dymQeWiYrtiCiHDx8kpb6sTuqrWAjn32KdwYCNLKtozhzmdgmgOuCzzcqqkBLqsZ8Kwm7Q9lGIeT5iAAAgAElEQVR1oF8g3XXFpaQs8eU/i83cPPt6IxdSlumfuiPeltrI+tnx46/G9tc+S2oqlWY7aDDQLa/YGNuvfxMDP44VJM/jDJeafOVjfxPblRTHyep1pFL3PMmAjN+aJu3+rveElF/hpz8Qb5v7MQNTZrey35gnqEgsvvW1sX2l5Nz82H4+2ycbYf+uN889QKfzFDk4ODg4ODg4wL0UOTg4ODg4ODgAWCx9BgCm2/uUuCPl95pEXkwFdP3l8xpyL9zHFzdmTtiG4Yy4/Kt07c+JwmZwOV12Y1ddE9tTKebheeo55k25/hJSZhOrGYRsbhfdj6kZ0merhFIpS1y62UakzhGFS1Nor8CyLrgHYCUYHjS4nlBv7T6rz4wHpHOhM7cuwRCFCYUvqjpVsmREBaPu/WI2rKecMoKSS60i9NbS1XTXXnLjG2L7sltvZRkn6Jad8jTwn1AAck/tNNt74oZXxPYVRSpZCrkfxvbOrQ/Ednk+dNkOrVsVb1uRkwCTGV7p+LzQv6KwaSeCwsk4eQE+QQwMUtHYa0oOv4TiTOglX5RyGaFUWob1XIuoqUqTv5dkbDYTXVQDmPIcmZzQVEJ5tKTfQ4MMSkqlhTJd7laCd+ZSdJ1LujtYycnXtp08fERLc2F5PLAwILn6hODICeXjCSVo+jw2LSwakarSeDrWNG+W1Jkca1SVJvRZIVqGMCDBVKvzpKhas1Sfrb6USqTpNOmzqRrpkxRjLiKY5R97j2ouQAmeOLGc20vsk8emOT/s30dquzbIsbdibCkAYEmWtN7ubRy76eWiorrixtheeI60SqvEMibjOPY/eKOBgde5jnSdRODICarmill5hm4hpVjUeXVdeJ/Zm2+Pt7UrvMf9T1OpturOu2P7da+4gec+zPxhX/wSabqdy3ns2G1UiM0cZtseb/OZ+2u/SRpseJTjqrTtR7F9z8e+yLJNhgEZL1/NPpG9lUtdGs9QWZg5/Gxstw5TZb7katKG9hvbYrujOlvMCHWeIgcHBwcHBwcHuJciBwcHBwcHBwcAi6bPTKxOClqqeJCAYUKtGMlVZsRdry7tjuc8EDf/UIZu66E2AwHOn2A+HJvm/ssvZVCxzIpLYnvvCXJdjQwVTgtlrvRfu4wUzeAquvNOTIuruEY3c14Cj5WaoV0XtY+6RK3caVvULlboAg3w2NScb61+02cGmYGwfgYHSVEaodJSoh4sz4u/XHO6FenmLkYKrazQhiWhIceXk7Yc28B22tckTXLfN+6J7ekKt08sZdtcexUVap7PfvDYE3SjnjjEIGU3bqDaZc1ScVOPk1arRPdalhxQ00I/TDWl/3pC9YpdF798K6Fq6b+L3hogiALU5Yrs354qQNm0yGVJQWkAzLqod5qRkrRcZR03hKJSBkzHd1ryzqUll1mjIbkFa6TTC3kqXFKi/mqKaq8hge40kGha+qLOPV7EZSZEnKII1Dx1forzTT7LOUsEXkg45M0L0Z7hvRihIjVoaqI4PeyM5O7zI1o0qJNaDSpsjyVZzgEbL9oU22mhP5/ezvE1+xQVQr4hBZYd4XxdKJLOnpKclDNTVItlDet+4woGaM0s4ThNDYb9ebnMB5O7qS49IgED02XeX7rAuT0oz9FWFV+gSzn6AwsrijehYYUSXj7C+W1Igtn6xyVfofT19sUhpeiv5D2225yjr38DgzpmV7M9C5IrbXCU/em6fZwPpo4/GNuN1nWxHcyzniUVIIZGRbErY2NgNY/NFL4V29WFqC0ynJe9y94S27UC1cLZebZ57bm9sd2+gsFDvYwo26MxvpgR6jxFDg4ODg4ODg5wL0UODg4ODg4ODgAWTZ/ZRKAwQtxX4j7LpXmZtL6GiYu3GQUCTEsAt4x4yu0s3a5+jVTMkovpXl2y8dLYrmTo1zsswcN2Sq6tgRLLuGGIgQBzS6k+y+a5ur5Zk3wxBe6f7aghxOXfEoWP1pQqddoS/FDVDxpcrt8peYwB0qnQfdpslGU722x4kLRGO837btaFEpFcUQMR9VYUlU9FqBfNRbdqI126JyTH2JJpKiKWCd02uHwlC19jeSdnRWFSFfduke03Xeb5lw/znoZWrI/t48+EigsL0meeBDpL1dlmSwckV5cEG2yWSKM2VfV1HmLPs4U1Bq2ItsoPs63yTXVtS2DCQbrRawscV6mS5GNC2Jet9Felfq3QpEpNtdtKt3E8NGqs27bQcLksy6LqVg3oCilDTdR1TaUUhArppEFLkJiJMaV56kR96etYltxnLyB9ZmCQjsqhwQVtj/khQdnL9lSa1Fc2sgNpj7aoYHNDDBhYHODcevFaqkQPzpBiaQxKuwr9GqRZgDGhdhbmSV+V5zQvIGm7bJHU29QJLlmoL4T2xlX8PSvBDm2J+zaFPkvJXGZMgguNoXXQV3QLEulJkNvlDE5bqFBxFUj+tkCp4hXRs0oerH52aWyPXkQ7MQpaoiZ8js+1Jx7j2LziTirUMin2ISMBTNv2LOpNjg0kWKvXGcsiNU2t4fOgNSDz1xzndExSvZwa2hzbxSzvb6q++LHpPEUODg4ODg4ODnAvRQ4ODg4ODg4OABZJnxkYpCI1RNMq7dPdteyLMifVw+VsI3mIbWv+NLrz62W6+EZGWezVV13G/ce5cn/bMbobt+2hWu3w3q2xPTRB+qOQoxvw0jVUNU2spBLi0A7ScCNtUS7kQvfwEQkq6Yur0hf3rQZ1VIWXUox+Irgc+goPBtnIfdlokSYQjzsmRiTnW4U/zEzRFZ4RhdZQpLbLNVXyw3u6ZCMVZyMrGTRzYReDvC2TnHY33sVAnK1RKlkW5rj/sd10B9+8mSrE8RVUGz639eHYLokicGTV+thefix0zR6bYVuPZkQxKDn4PFFwVCW4XkWiN9ZFaVVr9l/hAli0owCpOVVhGbq8vTTpjOER2mlRONVF7ZGOqDRPeCeT0I5KPj+ZAxoNoWg0RqOoyTylrBKp4XQMSPBQDZLZUpVf9zxWZ8o2Z3rYmg+qKco9K/28o7QL+paf0MbzoQbC9GQ+TedIZZUqnH80qGOryXGdivpvQq2ntJvMNxlR4E1OkbKYknFX1zEg18zJ+e08x/KC5L7KieKsKapCETRhQtRno8XwvrMS1LAl/KGRdtcgwSmhXxPtJ9fRXI/9RKeECRZN5vuijFMzy+dfom9qHryhUZw9eO8Hn/x8bH/lbxnI9ukhBq19zdVUjSVKkGefsy2lQGV3ZSll/mwX9dhTqTcrlFlb+l+iwiSvasrnRTNyzfMhtp2nyMHBwcHBwcEB7qXIwcHBwcHBwQHAYtVnxsBEQQjVjW7EDagBGxN5XjRXjznVeW3E1WnE5+6laY9fSnqruI4r5A836Zo7eORQbJfE9VsU1zPE9VxLS+C4Ih24E2tXxPbxA3T3FiQfkzcc3usxUVy0quIalPu0PeRkWi9ZqbuU11+Fiw9gpB36Hav0SiaC7eUloFuzSlVHNqMJp1T9E96LEfosLfc0MEyFS1rULvOiwti+wOs88qnPxfbIKrbH5vWkxtAQhaP0w4K0iSc+XZPitfKiWggiZZQXaPvRRZwusM9UZlnGsgTAs+oOL7LPBKJK6xsCCxMp5GyGdVIXtdFIXgK3ieLLFyVPqSB0Wya8N88nPZMcuaqMUqqC+7SEStNxn8hZ1+5OQ/kJGlDoeqVFEvKy7vnACNvV9kRJE9TZ5lVVNYn6DRHl0u6qxL0QMHEgygR7IEqpQKgEKwkLPVXjCS0eRPu0pe601nW+MaIEXrmKNPfKUao4F0SlWEf3pQGjOfardF7K25D5XfMrSqMlqLqoTdakOD/7muNN7qMidVSVvqe0aCKHHLqr0i4kPM9HMYp2mBeupyH65BlL2t4Oah5BGTOytsGcE3XL+WBo3ctj+7rXSy7Cx4VKe/zJ2F52+8t4TRmPqMux+vzQ6pS5P5BgsegWmFioXi8RUFPmGBWjylyrqzXOR7TtPEUODg4ODg4ODnAvRQ4ODg4ODg4OABZJn1lr4/xO6jq2CSpN9lellrjVxPMFE7kH1T2eylHpMzzMgIqDFzGQ2FyW6oQTomxYLy673FoG/3uqSffk8Aipm4FhqprSQ1Rb5Ty6KnNLqG4LDu1hOYPQhVwcoJt4WpQx9ZrknVKXv7hE67J/Ueoxl2Ud9AOeBbLRtQsSZMsXF+3UNIMk2rbUa4H1F0jQPt8PXaSeBOnTYJ6am2p4mOqn5WtIh428mfaM0KL7Dj4X23v27I3tYob1lJFrZTwNHEo7EdxO2iHtdXLmSJ8VOmZ2joqQsijkajUJGCiu+HSK18wLXdsvGBikENZFi+wHSkLd5bO06xVxbQvloO2fyoRUY4I+M2fhq+4WqO6UXYT+EGo5SAQrFLtHsi+b4Mnkj+hYPc543dvWtjlOqxIYVINNGsld2IgisZ7FbS4KFjYOKqjX1XttaHBQmWdTUjcDklPOi8ZARmnTFOfN6gKpwkaV9pF9T8d2eZJ2c4rHWsg5l3C+nm8die26nD/lCa2WYhnL2lcHORePjoZzfU1yTDYlUKvxhaLNcY6pzktQWqUYlQbqfxo7eMZDIR/SZxNDnAvmpJ6PB8y1WRqhijY7QMowWxFl5PEoGPFZ9UHe5NAEn4k3v5nPtdUZnuiffvDV2F64kUtWVPEVCH3W4DBBmqsTeko869XOeWSimtrB6yywbQM50JPlF40yy7LQ6K6CPVc4T5GDg4ODg4ODA9xLkYODg4ODg4MDgPOgz1rN0FemLm9VP+gqf3hKGen+GnAt2l9+LwgdtXLtmtguSu6sZo7qnokR0hYLEjCxVaFbPD9Ct+7aJaTMrlhHumZAKB0/Qzfn+Hq6M49PzrDs1VAhsXZ8WbzNC+junvdJuWi9NERtozmyarI90xTXYh9gjUEQ3aMGoVP14LTQf3pfvtB8Glwu6ASi9FUdxPszopgZFjfnbdcwj00dzFn2/FH6ZUvV6dierTF4pBEFR7ssSjBRRGi+LlW4lCXoZiui4QLpsymleaWdKjWWvSY0azZFKq8t5ar3WUkIhFROR2hWFTlhuUzKwfNIIZsU+5exQhk1uL2j2vPlHo0RFVEPNVePWK6nlJd2d8pMG8t0ocZO3t5N4Km/J8oiOevqQp81ZNzlRYlXyDG4XKkW1kFptk/fltaiFc0XGphQ1XhW5jnN35VQYsl4K0WHLh3i3JeTgHnlaeaGPLCHVMaaGxhwdfw2zpUn5jl2Dkyz3+8+SoVau0kF2fp1nH+Hi5zHD09yjjx4kNdd2eI8ENTDPnzwBPetCWeTW8p2slk2crlGespYDd6oATFfAP+A8WAjha2fE7WzBHjd/9zh2J6/45WxPbyUz7n8LtattzusWzsralkJbrwwQ+qyDbbzyCifccZnu+WGmFvS1B7gOWWZTD7HOh9oHWR599IuXM22bYiCsHyMZbdXRPUfsD3bR74V236Z2zUyY2oj87n5O56P7aAp4wKLh/MUOTg4ODg4ODjAvRQ5ODg4ODg4OABYbPBG2DhXmQZsVOVYWjiHfIbvXgXlIsQt3qFfjCiEskNUlo2vvTy2WwXme8nKOQZFMbN9gW66gwu7Y3vFalJcw2kJxij+tnRa3xXpWhxdQdfiwijdwOUTIR0xILmHRvM8d6NF96jma7Lq8g8kn4+4Kk23AFcXEF4qhexouJq/sZ90VEHKlpNcN/WqBkKTPFd10g3NKBCmkXxuShvOHWMwzfrxo9w+zevvnGFbLli6SzUf3oolUsdCke7efSC2p0p0u1bn6cZdMkB5RGWaefUqpUhNI0q8WpP32WzxfJWm0Eni3lVVS7ulCqn+f4O0Wi2cOBH2/XSK7daokT6bbpEimZOcgvmsSEOFcmnUwz6blohsaQkM2ZCAntqnkz23BwUmSNJnOjcobcdrBYnAfd3pMZ5S1WeqhJQS+tyelUCWXiIgJfvZQL5Tvm53c/6wAJrRfKFCqbQv7SDB8FptnVt4QFPqtRSN00qN7etnSKsEPsfgsUOkXkotCTw6xvk3vZpU2uh6zo/rxknFVkqka5cUSRutGGdAyOyEBEgdIGU2LDn47NEwIO/xgxzfGaEz6wVSgkemSZlV6yx7QerOSkdp9i1/HWGtRSOKMKhLJxqSAyx/mGOzImrCsfUSKHcH+yCefQQAUHvi0XhT7s6bYnvb41+K7Z1bONe+7I5fie1CgfVz/4++GNvBRQyU6xc4146kRLl2LZ+Dn//uR2L7yj3MV3l4B5+/u+q875evD9s/KAlt//2nYjtbZ720ljIHaXo9yzX5ib+MbRvo3NNRnZ47nKfIwcHBwcHBwQHupcjBwcHBwcHBAcB55D7z0hGt1BKqRyiSgrilh4ukKkbF3Tmt7rHI/S0eMLTTpK6CtLizxZ0eSI6q0gxdrZilOuziMR67ZISuN0hQwuk5uhYnJhgoK5Oi23BgjCv2iysZ8OroiV3hNqW9MhJgC6IMEJe/5ofxeri7/X5FhutcKwgwWQ3dsaokHM1L0EgJQDglKiYNypkXR2U6CGnEpihjhJnA9IFdsT2zjyqF8iCv+eAWBsc8cJjteu0m1vs1l10U24Fc6wePU73y3NPM33OVuINHRF104CDLg3LYJ1oF9tnDs6TdjkgerAWhILIe+4kG/mtL/qi2399AnADQbLUxGdFnnuE48SRIYaBBD0vKNdE0CTYhGpvSR5XOSaX4R6PVXRGm51Z1mPG608YJ+kfOGQiVlVAMJc4vhY+KkPI1iKbYmveuwb5dKZEKTw8IXZSXgKuNSPmD/lAvxnjIZ8N+1RJlmX7KNlqkUmwP5Z+iGVXIjCSKmhjkMoWmUMxTZVE51VgHK/OkT1aOc1lDeoxB9crQ/nZpbOeV+pPHz0Sb29fmOU4PPMFAkYfmw8CtNVFJehK8V9VV8zOk6L0eQT5Vbdu3CJyCoN1CpRSW65DQ8C15bmbneG9bdlJpu/amW2O7/kOqvDLlveG2e78db8vdcG1sX7n59tiefeovYvsf/uKPuN1nHxrfwDa8+zU/ze2i8jagvfE1pOGOf/dTsf39738+tlMTzE/6zl/5n2N707Kw7Rrf+Vi8zT7Ne4bM6bVNl8V2o8B2/sRzfE7MqZIVi4fzFDk4ODg4ODg4wL0UOTg4ODg4ODgAWCR9ZmCYL0r96PKONSCu7SFRYuU8ur6mArqoY+WJBnTUvESqTBHK7mCd9o5pun79EhVfV2wk5eIP0t6+wDwzM/P7YrtYp6s4NcCgkalBKj2WSDDJw3tDRVu7QpXFoLjlV0pwtJmSBKpS9YN4xxP55Nr9VUU0Gi3sPRi6dLNt0kE1ycfTFp9zaYHlaYmSblTy1NVaoQu4XqQrtiB01KHDO2N7yb5Vsb3m2tfH9s+8/qrYXmjQpbpEciotHaVKxROqdflGuvTrU+wTwRz7xM4HfxDbszN0RyPKNVeTwG5lCeoHjw0l6dYQSDu12kIzCfWYzoqCpE+wsGi0wnZMGVVk6bhSOiERyVBOpDRY59z83RjN7yaBOzWIZkvpCQ2uqNfkdVpSzy1J3JaSOkxJpbeEvgyEgtCgsF7E26aEq87I+TK+BokUSkMpPilj03KftsnGe/QF1iKI2tIXilLlbi1RLiUD6Wp789BWpKqsSPBNf5jHLfE4vo7uJZ147Dgp7EaL+QfrZc4Tay8nxVGY4NjUoKV1qau6BFatSTDcXU8+E9tHdu/lTs1w/NYt+0A2r2sQJMeZ9CVPFM1t272OlF7tF6wN0GyG9dhuCQ0sAYKbddbnd+//cWzf8Suvje3Bjd+JbX9r+MxJ/eDT8bb6Netju/iOn4ntO//VH8T2xv2HYntWOsjFa7mcYWiIbdirh+eXU314+8+8P7Zv+Sk+270Un39ZmQPNwWcBAKV/ZNkLEpizKflOB954W2xve/CR2N69W5TdiTG7eDhPkYODg4ODg4MD3EuRg4ODg4ODgwOAxdJnBkhFgeg85RAkmF3GZ5CuvLh4F2YYnKoh7nUvNcSTd7ZpMCbxtQYVnu9EiS7eI6I4uGUJFWSXiItvus0y5sfpnpuaYtCydlNcsuKI03w1xTHScGPLwvPPS/DDIeHDBoYlf4+ceUGCOlZFRSIxD9Huc8C/dhCgFOUKS0mQzVpNcl+J6z4nNFkj4a/k/U6WwmPL0h+uGiVNVj1KF/GTTzHomM1RSbJ56OWxfdGmq3mdLK9vhQYxltdaKuWanyal+egT98f2zuefje3psuR8GwjPPyf0jcRuRCFNGhCSz63SEOWh0C1t6futpvAF/YK1CCKKIJDelpJ+ZEV1IxkKYRK9M5GULPwvIUnjuEglchtyuzAECde28jl6zUwi2KcEj0zkCFTKTOl12r6UoRNwMid9Oyv92ch1KhqAVIrrqRpUviPz2VCN6hmdLy4krCjbhM4UVY5JUJ5sTU2zp2q/eqQ6OyFzZdaSYr5ogsqyiQ2koY/v41KDQ/u51GDyMNU/u559IrZHlnJuHRwhjR4ILT07TUpu+hiDiJbmSJd7GmgzEz4j/KWSV2uIv0/PUkGMQHL6gXNDIk+fxhH2+9WGAiPXTDDISlWzH0/uZfDM7ZN8nt141xtiu7rrEwCA3CznufoXPhzb/uUMfJu+5s7Yvvgqbr9Q8HyqvIuSt1SfenZ2W2yXPv1fwnJt5XIKK/N47SpSZsEg7/8L95BuK8kkk0s0YfiH1yvp4mngPEUODg4ODg4ODlh0mg8D3w/f6JryJpaSr7WhQBZf1iQzfEUy38rbecoPvyY8+aJtSEycyT0MFe7n+ZUz0ub1r5XFg6MS/n5hRrIkS4yj9fL1uHKMb855/SCu05tjLM+ZzdFesTz0gpSep9cjneI9Z8S7kpU34WpDvl5lcW5GUyg0zyfiwlnAWtgoZk8zQy9IKith3TOs40ZW2kdTgehi7HJY5gMzXPiY97nwbuNSpgOYOc4v0Ie+9Y+xfey5rbF9zW1vje2ll69n2cVLaefoaTu+Z3tsP/nwd2N7715uL0u9Vgts+1LU/44t8Mur0tQM87pQVDwY7VM9K6EtXpl2n9sSgPF8ZHPhV1VWUpV4soi+3tK0HERK9lcxQyPKRB7IWPd98UJBPTMiHhARRq2lC/TVP0W0xQtUKXX3SqnnTV0gmrpDP/o7QyknqXtSHo+rS/qTssQpQkrjdHVfYJ6J+p/pY4b1TtoUTamj8ZzUW+YlPB9aZi1fuFNL+uVh8di02/S2XLyMYpL1Gxk7aJZrdFGXDOhtWSQ7fZzenjkpl5E+FFhd8M76nsjTKxDIGK/nwv45W+Q5dkwzllJ7gfcxKPHlUrKQuVxlG2u9qLiln4jnj0RMHRVESHyuCp9z33uEXu5N7/qp2B44EHpZmp/7Xrwt88ze2C7/8e/HduHnGVModfM7WaZVTH11oTQDtsy2aD3zQGxXv/FXse3f+1hYFk5HqF65Kbbzv/ru2P7i1/8htr/5HBflBz7nrFSKlVrMh4u7j0rcrbOF8xQ5ODg4ODg4OMC9FDk4ODg4ODg4ADiPNB/WC92a9RrppUBc0Vbc3OUyqQ1dwGsGuRiLizjpv5ufomu0to/UB+Q6eguexJrYLVTAAXHBerKY04hbPJsTt2VWaSSWMZWmSzYDnrNyNHQb++Iars8LTTjHBXPpEZ6vIOVNaxwZodJQE7qgD/A9DyODoatxtsK2rDRYBxslK7YuRp0XanFYsigDId1Sn+XC8+ePMxZQKkV37cYVkqrjGP3yR5/ZKjbTdmhKCU/iygSyKL/RltgVGsZEFnJnJG5SmevncagUumbnG6T71LWu7u2G0GdKYyTW9gkLlPL6H6fI81PIjYbZwpcPMS1NusX7mZZFmeUax6am8WgZvZ9wu6Zq8RILf4VKU8pOyuX7SqsJHSaUYhDIEUK3KT2l50msjdUwPpJWJ97FKiVdF5v1YiRmkS+xl9IZ3lNWaHm/zyIIC43fxu3tlsZk0nbQtCqy8Fzp3KguNVZUW+L+nFjgmPUM62ZcMtcX1zK7/aBh9vJmVRZOT3PhdLnK8ShMPPy0CHOGOMdkJe3IgsxJ5Xp4zpkFzqcNEdpkPbaNzppW2ttLpELROuhvPDhe9PQbdeG8sEE4sY2xoT55H2nNX3rDfwAADByU5/AjXPBeeJbzrv2TD8X23GVMf+TfzfhwhauYksNbxjbvyavV2F9qWx+L7fqPfhTb6fvvi+3MNPf3vXAOrl0qfes9vxDbz+79fmx/9ktf47ll4fyALE3JCH8cRFIJbxEJP5ynyMHBwcHBwcEB7qXIwcHBwcHBwQHAIumzIAhQrYduS1WDeBInoynpELJDdHcN5UidVNV1Hrky2xKuX7MH+w0J+1+RLMlCYShV0da0Cz1co0Zc8QtG3Wzqi6cb3UqMDRGwxG7/dpuu3LYVynCe6reSZPht1IXWa0mKDVEHaX30A8bz4EVUUrNE9/fRE1zhP1GU2DyiYtLM3a0U7eHh8B7zk3SVzguduGuG920GSfGsWsUYKabFWCjpDKm5ktCvvipvMmybppFUEGN0zR5rUdUyLUqjhRm64+uN8P6UltCUD5B+pak9NEaM0hiaYkDpvn7BS6dQmAhjxKxdzXgumCOd4UEyq09xe0XSZtguMW8StKD83obuK/Sm7J5R2kvrVmM6yXk0072qc6Dn13qWmSyQOakzP9WspMPQuClyjqwoLlOiPsukpOyJ1Clhe5p+pfmIzg4AgcyVRtNTaIoVmSuMjI20xpGK/ld6KdDUGzLd7J/hGDk4x/hyWZ/zxEiRY2p0eCi2M+tJsQ1ozCSZu0s630iJpo9w7DeE3vW9sH8GEhNtIE3KLLBC/wZKbfOmEqlvZPlCo4ci8kJCwxQloZSeTjZSVqmHbfd9O7Y/Uw377LvfRZVZvvjnsd380Q9juzjLuXPgoS/HduOJe2O7uoapWpqjXOphdO7ScVflvO7vY7yhnKTZysiygZqothvXhHGw8m8lZZ/7y6EAACAASURBVPbUYS6T+fBffyS2d05KfLI01ztkpf0rTVGKd/ZdhODXeYocHBwcHBwcHOBeihwcHBwcHBwcACw6eKNFO6JRVJnTFpflpCpAMkKTSZAuSFBHE9FngbgP5yTgWnGUrtnBogRD1MzWVaolmuK+a0kQyIwENSuMMvw8JGWEulsTWaiV7lMXfaMVHSdqBklzUhL34ZwobGpVnqOgrm9Nz+D10zUf0hT1KHijqm8qdZbt0FGqAIeHeF9GOIupee6fi9RzuQxd642iqCOEjds1x2BxVVGjrBbFz7IVTBGSlXrPCpWSlQBtDcmQPSWx3+sLkjpCPgdS4D2NRX2uIt8LC3W6/CslUqGaYT2VcIyL614DD9pF+HLPFQbwo6rIFURB1eKYKedZtycCpZo4fpKMs41OrTRW+5TfAUBj4Hmyv6ep2jVthlIrek6rth6rdCQ7ko7repfAk6pgM0KNpTNs+4z0obQGbITWkWSXj5R2/WpVAxMr/xKpWSTIZa+AjVqmQINLRnWv9RUofSOPhHZiOQLrtCbqzhkZazNC0Sp9pWlvstoOUvaq7N+U/qEUaRzI0upcrale9HnCcydmUDlfv5cmnAwLwHZTLNquZqItWhL0t93iHPT4j0MazOB18ba7f/V3YnvJjVQHzn7yK7FdOMzgmllVYO6h6tfu0vrRJSXdx7LaLXmezo8zJZZ3x9tiO/dzrwUAbH2U9N1ff/xTsf3wQdJ9gS+Bk4XJ82WZSiDjYiFSry5GVeg8RQ4ODg4ODg4OcC9FDg4ODg4ODg4AFk2fATFFIIHbmuL8m5Ugf4GsUNfwdVZUQmmErnB1H1bEHdqwQod5msGdZ8wVRE0m1FxVg2NJ8Mb6oETtK7AsbcmwjEADwSXS19Nshvu0RDXm58W1L275vHgeGylRyAU8t9JC6HNQMd8zKGbDOiwrZSDu2rRk5bZpyUWUkpX/86QoU7OhO3ZkgPTkqtXMnRT4rBsVfXjiCl/Iky6tC0VakICfbQmyiSzLVcyyvHnJb7dWcrtlcgXZR9RqUbb7gwukzJ4TVcX+Q3tj29Y015IosMQtrznWJmv9d9fboI1mOcz3M3eC9zsqcslA+rf2WbU1kKOJoiT2SjitSkwr6sSEOE9pRKGZNWeZSVByUp8JCpnlSgsFlBEVVq3GCzeie0qllXLX40RlJsFZfV9oc6UYxW42T1UqXkho8EZfKTCpv5QoqHTpgQZnlGEVq6+8RN44/t7qFQxS0FFoAkATQif2zLcmOcYSciChBBOKQKEBu2WTVwWZKrQSu4oyVdV32laLyKB+PjBgnWo1JDK5S249VXy1ZalCQgwbUWlPPfrNeNvsPAMnXn/59bF95W/9cWxnDjBPZ+mBx1mW48wVlp3l0oaMqLxbQ+OxXc9zzDQLnFP9SFkGAOmbb4/t5xfYFs/eG5Z5/48ZpPHgrChQVfEmk4wvyxbKddJnC3XtlyEWMzSdp8jBwcHBwcHBAe6lyMHBwcHBwcEBwHnQZx2Xn6pH2uLmViqtJRyJL6vSbSJfVOTW9UnbGAnGFYjiwMo+QUtyUZVJsTXqvH6zrTQcr1lr0BVeEX9mXVzktQbPqW58dfR3lBaaLyknAcbS4tgtqspCEgHNlbm/evwKmfNgOM8C1pIS0EBwVmifeQmYKHHskC6SymqVJDBYRE1tvpjKh4s30U5LbqO0UHC+tGtKKDtVuWn/gJR3QOppTFy6WQkWpjms0kK7piVnUrUS0oBPbmeuoakyg1A2alR+BE2eY91KKu1qVe5zYoY0XP0Yz9M3BAFspMKcm2IAzmYgbmZR0KkCR6kNK33dM6fSZ0rJJEgIDeqoUjQ5OEjQbabrPprHMCnv0txnXldbc1oF0RTnpyTgW5YBVLNCqSbPoVSQUhc6r1SjfftDcVtr0YzUlkplKb1Ub8rc2iMZnBEOoVP+REZFqV9VjbVEbWuDHrSanjvBVShNJQE3hca0Mou2m6p81MCdROeZo9dPy7mluGgmWDqh0pSak+2+6X9gVYteqc9EbaeKTaH92kjwbTSjYz1Rjh566nhsP3Yf85EtvfTS2L75ptti+6ZfenVsj+Ykh+XRp2Oztn9XbA9f/arYNhMcS3OHqD7ct/NwbH/nsx+L7We2cSlCfiQM8DmmDxW553xOxyz7TaPB+WtBAs6yBoDxsfA+pubPfc51niIHBwcHBwcHB7iXIgcHBwcHBwcHAIukzzxjkIsUO+rKzYn7tlKhu6uqAcGaSpnJSvvIg6a5hSbEbZ0TpZpXoaOsNUNXWqsmVFeTdIHm3vGrklfsGIMSDgjNMiC5vmZEkXKkSipEVWkd5iYrZS+IkiYlrs+GuEqH1WOrwedEtqOKrH4gsBaVSHEVJFRBEnxLKJbGNJWEYz7pM0/erwejurxyw0Xxtpe98q7YzhSpLPOMBmITd7HUwYLk/Xn+INtsXmiLhqhgxunRxfiE0HNgnjUYbk9QE5E6MaEeFJf/aF5UitJPqtKWszImKpY/jAvd2C+YwMKPxsdcg+02JzRwvckx0xC1WKKnKc2AUxUzRsZ0Ut0jqi2JoKf7tCTImuaf6kX5NIV+V7VaoAEePaVbha6JAqem0xqkke2QUjpf+pOBKqCULtL5q3P9/gfl1ACLen86/yr96en+ieCM0b6JHG7dAyp6RhVh3fPFWZy6BAJItreWV4Z4QiGn6kSboPtOpQ0T06bsq/R4uy7l1SCUOp8mAg/2d54FIvVZdB3TQ0FnE0ESpW0TdLKo0qL8k0od+qLxbkkQ3icfY2DGZ7Ywx9jnCty/MMjcpBtWMejiRddxLv/5KzbG9vTj34rt//UP/6/YPjoneeWMKkm5fcNgdH+yDEKX42RTvKkBoc+mq0rlapBVoUPPY0w6T5GDg4ODg4ODA9xLkYODg4ODg4MDgMXSZ56PYkQFWAnSWBMlRNvQJScevIQLPp1SV3j4QyCKFU/yDGGBgfLKU1zl7okHPSuUx8A4FU4QiqbVYHmrC6IkEkVOukWqoThKVdF4nsEIK3VSeOlsSO9ks0opiEpGXIJKpfniPvZEiXZcKL5yrb8Bxqy1aNbDSmwJ3ZKWwFkpUWo1qmyTY5ITLSs0RDOiZDKDpKuGBsd4PqWRpG9Uqrz+ZIXKqT2Hj8T2tp1UE5SkDZqVPbH9wMKTsX3nXTfG9sTwzbG9fgWVGJmc0jZhGTRWnhFaODsgAf4gOdHKLEtpmvaCqAqH8v2nzxAEQDksV0nGRqMtlLMo0Zo6ZoU+7ZbbTL+gfA2slmAhNGeZKJmaoq7soQxS9VRLxn67LUElRa2WaQtd0lYKQhRZ0ThUZaWfUmqwe44upekSKiC5vU4d9ApyeP6wcR5GP5EkLrFLDE/qT5VVqr71ontP+Vp3kruuB11vEiovCbCX4FS1jEqdShufmoYtPDR5ta5l6CjgWqpslrZOaYA/pex6UXMJurT/gRwNgGxUrEYPtk7pQj9BkypNeSq9GKhSW9WSntJuEqhVKOzZWT5bJ09MxvaxE4diO7tc6O8Tz8d27ZEdsV0+xufmsSavW5DAyJpHcCFSf49kdN6h3ZAlMJMzktdMgzSqYlb60/RMON+1W+dOozlPkYODg4ODg4MD3EuRg4ODg4ODgwMAwCwmb48xZhLAvgtfHIczYJ21duJCntC15YuGC96WgGvPFwmuLf9lwbXnvxycc1su6qXIwcHBwcHBweFfGhx95uDg4ODg4OAA91Lk4ODg4ODg4ADgRX4pMsa0jTFPGmO2GmM+a4wpnPmo+NhfNsZ8eBHXXG+M+fnT/L7SGPO5cz3v/19gjPk9Y8wzxpgtUd3f0ufrfc8Yc+PpthtjvmaMGTn16J7nfKsx5ncj+25jzObT7Pt+Y8x7I/uDxphD0X0/aYx5k+z3H40xO40x240xr4+2rTfGbO117rMs68uMMR85XX81xnzbGDN6PtfpJ6I6tsaYTbLtdmPMV/p0vdKZ9zqr83zMGPPOC3GufuAlPhbfFd17cHKZuo3FaPsbom07O9eMtu81xoyfbZm7lCttjHn8dOPdGPPfjTF3nsM5X8pt+4dy3/caY1ZG240x5i+i9ttijLk+2n7ec4kx5ueiOv+gMebfd/k9Y4z5gdHszj3wYnuKqtbaa621VwJoAPj1F+Ca6wF0fSkyxqSstYettT+xE+n5wBhzK4C3ALjeWns1gNcAOPDilgqw1r7JWjt7DvvfY639UPTn3QC6DtZoAPwqgE/J5j+L+ty11tqvRfttBvBzAK4A8AYAf2XMBUub/UYA3zjDPn8P4N9eoOv1A+8G8KPof4cLADcWsRXATwP4wUn7dR2L0Xj8S4TjaTOAd5/uIX2OuA3A/WfY5/8G8Ltn2AeAa1sA/81ae7W19loAXwHwn6PtbwRwSfTvXwP4H+d+Fz1x2nnWWtsAcB+Anz3TiV7slyLFDwFcDADGmPcYYx6O3jT/384DyhjzK8aY540xDwN4RedAY8yEMeafjTGPRP9eEW1/tXgFnjDGDAL4EIBXRts+EH3B32OM+Q6A+/RrIbJ/GH1FPG6MeXm0/fboDfxzxpjnjDGfNCaMOmWM+ZAx5tnoTfi/v4D1dzZYAeCEtbYOANbaE9baw0D8tfUnxpino7rvtEWvui0aY/422vcJY8zbou15Y8ynjTHbjDFfAJDvXhSi86UX1fdzJvzCfz6q19cYY+43xuwwxtwc7f/LxpgPR+3xVgD/LWrPjSed+k4Aj1tNyNUdbwPwaWtt3Vq7B8BOADfrDsaYi6L7vCm6/heNMd+Kyv4+Y8xvR78/aIyRyKG4C8C3I3ulMeYb0b38iexzD35CXziMMQMIHxq/hvBhpRjoMQZuMMZ83xjzmDHmm8aYFdH2fxX1oaeiPlWItm8wxjwQ9b0/Ok1ZftuEXuWtxpj3R9vWR33tIyb8Mr/XGJM/6bg7jTFflL9fG/XNFxMv6bFord1mrd2OU9FrLN4MYKe1dnf0gPt0tK+WPW+M+XrUz86q/BHeAODrke1360vW2n0Axowxy3FmvNTbdl5+K4IhRt8G4BM2xIMARjpzg5Txpug+N5rQ6/NxEz6D9xljflrq7hvGhBGijTEGwLUAHo9Os9mEz+fdxph/J6f/IoBfOFM9wVr7ov0DUIr+TwH4EoB/A+ByAF8GkI5++ysA70XY0fYDmACQQfhm/+Fon08BuC2y1wLYFtlfBvCKyB6IrnM7gK9IGX4ZwEEAS6K/1wPYGtkFALnIvgTAo5F9O4A5AKsRvlg+gPDBMQZgO6jqG3kx67dLfQ8AeBLA81G9vlp+2wvg9yL7vZ06Ok3d/jGA93TuMzpnEcBvA/jbaPvVCGNW39ilLN/rbI+uPR7VfQvAVVG9PgbgbxEGg30bgC9Km3Xa/mMA3tnjfv83AL8pf38wutaW6Lyj0fYPd+4l+vujAN7Z6QsALgPwBIBr5Po7AQwi7I9zAH49+u3PALw/sscBfFeO2Q1gGEAOoTR3jVxzB4CxF7uPdKnDXwDw0cj+MYAbzjAG0tF+E9F+Pyv9YUzO+0edtkH4UvjeyP4NRPPCSeW4AcDTUR8bAPAMgOukz1wb7fdPYL/8WNSOBsBzUqZPAfgpNxZfvLHY7dpnGIvvBPA3sv0X5bp7o/J+W/rRWZU/2vdhhHN9z74U/f0RAO9wbXvmtgXwvyP0jm0Fx91XOvcY/X0fgBsRPZMBvDwqy9ro9w8i9FCnAVwDoALgjdFvXwBwd2Rfj/Blq3PMjwFko3udAt8lfACTZ2q/F9tTlDfGPAngUYQvPB9F+GV9A4BHot/uAnARgFsAfM9aO2nDL4XPyHleA+DD0f73ABgy4Rfu/QD+NHpbHLG9PQbfstZOd9meBvARY8zTAD6LpPvwYWvtQRvGin8SYUebA1AD8FFjzE8jbMSfGFhrSwjr9l8DmATwGWPML8su/yj/3xrZver2dQB+N9r+PYQP+rUAXgXgH6LrbUH4AnIu2GOtfTqq12cA3GfDHv00wjo+F6xAeJ8d/A8AGxF+VRwB8H+exTkmEL6w/4K19inZ/l1r7YK1dhJhu3852q7lfB2Ae+WY+6y1c9baGoBnAayT344DWHk2N/UC490Iv8oR/a8erW5j4DIAVwL4VtQ3fh/hixMAXBl99T2N8GXrimj7K8C+9/c9ynEbgC9Ya8tRP/48gFdGv+2x1nZyuzyGk/pJ1H/+HsB7TLim4lbQM/CiwI3FC44vAfg7a+0nZNsZy2+MWQVg2lpbkWN69aWzGqOubQFr7e9Za9cA+CSA953FOS4H8NcIP1b2y/avW2ubUbl8kCLTcqqnDwC+akNP4wmEbbYsKlMbQMOEjFFPLCr32QVE1Ya8Y4zIFfZxa+1/PGn73ac5jwfgZdHDRvEhY8xXAbwJwP1GFu2dhHKP7R8AcAzhW6qH8IWng7rYbQApa20rcj3ehfDL5n0IXYs/MYg6xvcAfC96OP0Swq8AIJFJKba71m3UTu+wJ7nAzfnngdJ6DeTvAOfeX6sIJxEAgLX2WMc2xnwE4dcJABwCsEaOWx1tA8IXnv0IH8rPnmM53wjgT3sc00byfnJReX9iYEIa8E4AV5kw4ZIPwBpjfifapdv9GADPWGtvxan4GMKvu6eih8Tt8tv5BEw7uRzdqIS/Q/jiWgPw2dN8IL1geCmPxdPgdGOx13Yg/AB+gzHmU9HDHTi78r8BwDdlv9P1pbMeo65tY3wSwNcA/Bf0btvLEH6k5hB6fw+fXE5rbWCMaUrbajlfB+AdJx8T4eR5Novkc/wUvNieom64D8A7jTFLgXBiNsasA/AQgFcbY8YiLvFdcsy9AH6z84cx5tro/43R2/B/BfAIgE0AFhDSHmeDYQBHorfpX0T4UOiJ6M1+2IYLeD+A8GXqJwbGmMuMMZfIpmuRjLD6s/L/A5HdtW4RTiS/GQ1aGGOui7b/ANFCdmPMlQhdu/3E6dpzG6J1alF5lL9+O0LXLhB+mf2cMSZrjNmAkCp9OPqtEe37XnMa1eLJiOrlaoQelLPZdzlC9/ZPEt4J4O+tteusteujL789oIemG7YDmDDhYtOOsqfjERoEcCQav8rt3w+uV+rF+f8QwN3GmIIxpoiwTX54tjdiwzUdhxF6rv7ubI/rF17qY/E06DUWHwFwiQnXn2UQ9pd75Lj/DGAG4WLsc8HJXobT4VJwzuiJl3rbnnTvb0NIXQMRTW5CvAzAnLW2k+17FsCbAfwfxpjbz7ZQxphhhA6JqbPYdwzhWq/m6fb7iXspstY+i3DiutcYswXAtwCsiCrvgwg70f0IG6KDfwfgRhMubn4WVLG934SLMrcAaCLs/FsAtE242PMDZyjOXwH4JWPMUwhfqHp5lDoYBPCV6Ho/Qsj7/iRhAMDHTbQQHCEd+EH5fTTa/lsIX+qA3nX7hwjpxS3GmGeiv4GQohowxmwD8AcIXdD9xKcB/I6JFued9NvXEbqZO+gs0tsC4A5E92itfQbh+oFnEbpnfyP60kP0exmhmuQDxpi3nmW5bgDwhHzZnGnfB38SvBcn4d0IuXvFP+M0i8IjavudAP5rNG6eRLhWAAD+E8KPm/vBiRII+9tvRF/Uq3qc93GEX9oPR+f4G2vtE+d4P58EcMBau+2Me/YfL+mxaIx5uzHmIEL66KvGmG8CvcdiNDbeh/AlYRuAf4r2VfwWwiUZf4KzgAkFPBdba587i33TCB/8j57FqV/SbYuQoek8d1+H8D6B0GO0G+F6zI/gJMVt5Ml/C4C/NGcfwuC1oJDlTLgDwFfPtJNL8+EAIFQmIFyQd+LFLsuFhAmVGf/BWrvjBb7u7yNUy3z6LPb9cwD3WGvv63/JXrowYZyoJ6y1H32xy3I6uLH4wsAYcxvCRcxnDAVjjHk7Qon9fzrPa+6Fa9sLed2/QfiB9OBZ7Pt5AL9rrX3+dPu92GuKHBz6jd9FuBDwBR2s1tqe0vIu2OpeiPoLY8xjCD29/8uLXZaXMF6UsdgL1tofIfTonw1SODthxksVL9Y8+z+dzX4R5frFM70QAc5T5ODg4ODg4OAA4CdwTZGDg4ODg4ODw4sB91Lk4ODg4ODg4AD3UuTg4ODg4ODgAMC9FDk4ODg4ODg4AFik+szzfOunokN7rdM2Xc0Euh/afW8je1vZR/dOno9/afTPXvvr22Eux2pZs259bNfa3GvyGGNF1Sth/rt2mzGh+rGAvdVsnLDWTlzIc4ZtGcak9Ez3d+REnekfUq/djg1sIAd2PSxx9kSU1sT+Rjb32r9N82yq3vQ6TxCdQ/ublvZsIsl2C1gLWKmPev3CtyUA+J5nU1F7alktut+P4kzj9FzvfNEXOnmfs2hP2+PgbpfqNU8otO48T+pR+krGD+u5Vm+g2Wqdd4jhkzE2Pm7XrF8fXrfJEFYLJebbbFvOVbk0j63VGrGdH2DMvfLCHAAgCNgX2y3OW8MjY7Gd9hmrttJgkOBmwLGWzzKQcTqb4TnbLG/KY8HKs5w3j01SlV4Y5nVHijxnaWGGZaiG97R05WrZ94x5UDE7zQxOpSbve2R4KLZtjYGqn9+1sy9jM5VK2Uwmc8p20yM6tc5BpsfzNIj2sT3mS89077t+iuVISf9uNtlvdP5OPs9M1+2JIdtrEpYydPqg5/HZ4fUou6It5dJxauQ8nXm8Xm+g2Wye09hc1EuRn0phyXiYAkYneq0WfUj6nj4YuHfQpaKN1z1odO+Xou4NBLlmqkel69WzPv+6/DIO0D//fxj8dutMIbY/+hcfj+0dj4dq6tnpI/G2oMWJI3El7WhQaK/vXsbjh/doVNQLAj/lY3xpmPg5l+JkZKQZPClzIBOlSXOyy2SKPCBqh1qDqd9sm/WR9tntvBTPkZY+I3M2UlIu6/HYtNjtxkJst9o82DOsQe2pnpwzneY9terV6Pp8ULTkTdCX/pmcnPQFUCaKgJNMu82Jd/uO3Re8LQEglfKxYmI8tKXf60TS1soVeHpDOnlFt6NjMCV3rz1dx3TiMonJuetm2IDH6otI0KO8+iKk9+Shy3jXfmC1n7H0RsqeSbGdswX2lSDFPrdycBgA8OS2bsnezx9r1q/HfQ89BABoTM7G27//A2almG+Nx/bFy3lf259n97rmla+O7Qe/9zUAQGmBfXFuitky3vLWX43tlSMc008c3BXbhyp8Kbtiw2Us74YNsT01F2fUwcQQ43E+8sWPxfaf/uXfxvZ1b/mV2H77LQx8ff99jB36yNYDAID3/cGfyL5XxHbiySdt+YXP/GNs/+goMzy8/U2vje36Ngaqfs3db+rL2MxkMrj44vDeUr70UelrOuE3W3yxTMv+admpFr181trSd/1sbBeynF9bco6hpcy2sSTD/n302N7Yruj83ZJnQMDy6gt1ymcL1Ouc9/RlTF9cqo1wn0Ke189JXRTS8jwyvP5CjW3oyQt3Psf7tu3w3E9tOWMA8lNw3nGKEh6CxNusvsR0f2u0XTw4vd4Oe3046ouQvnEmHoH6TpKY+GnqpKovelrGlLwEpPThGO+i99P9RTCJ7i8/psfbd39ggKgNs0OcYPN5vgAWM+wmgdf9HutVftG12uH+w6PMd+rLcV6Pzx6T6Eum63a1tb0NRllGeejpS0mzKSlvPA64bI5fm50XwPIc76du2O6jI8Pcl2dDIF7CZpWTyaw8cNqt/ucHtgDakdfMtvUFXN8+uo+mxEdKV6cdLfWLJL1qPT5pe+wfyIuQSZxH27Z7n5ND4clbvN/DMxzvK32xJm90+lJm5YHULPEFIpvndeYy4Xnagb4WXjiUSzU8/ED4wlXMsX9tGGNA4c/cxwwV1TpfJq7ddGlsHzh+MLZL0YMjk+cLz81L2aePRZ4kADh+gKn+nt/BF78dh5maas84w9Lc/Kafiu3BAuvphjUca4OvZEq8B+79VmxfuoIvTktKHHvLqkdje40Xenx2b3863la/iS9F2e6PIniZgdhecTnzuealvbfNnTFLxAVB55syJQ//vHwYNhLeId5FWuY6G0jg+3Z4noy8V7XFa15ty4tKi+dOS11lhtgXvCmeyJNkGA3x/KkX0E84HLp//OtHin4cF6KXqIG0PF/kuGqd4y7x8SLXHBLvZEMavfN+tpjnp1tT5ODg4ODg4OAA91Lk4ODg4ODg4ABgsfSZRexJT64P6O46twn6SlzbCW99tGDsLBZMKm2i52sLr6q0np5RKQKvx2LblK6bkdfGXI7b01mh57xOWbhJy+X1WNId9FwQ2oPS6Bc6tyIUxNBSUl8jA8LtatnEpVmvcNEiTOiiT+fptvZ60CeK5JIWbb/udKbpQdvYxEI8Ka+4fZWvV3esF51nYIxDY1A4ek/6BsSNHdSlLLoeRsrSbHZfG3Mh4XsehqPFp5W6UHpy7QRDnVyhKXaXfXouCJU/eqwXSggfdAzqLkrNmu5jObC6D+/JlwsnJjVz6nG6Rsn06ItBkFgxxnNLWZaOLwEA7N17FP2ADQLUSiHlarK8q6lp0mFHDnH5y5rN18R2fnxpbM9seYonjfr9q25hvs0Vsu7k8z/6YWzruiqvyXVEzSkukH5kF6k0ZElDb9zIxdB2M6m88Yllsf2um0l57wh4HxetZNmXT3Ct4KZoveyDTzEP8ON33BXbV63iWtCijPX6LCnsDdHCdQC45lKuqxkNrke/YWHRiii7dlPnH9azzvdtob+T/VfOGe2ua44gc1vQ4vIBI31a12YWV7Ee0pNsh2CBC9QbDS49aApbnJEF220pe0uexfkUy9aUsVTMhc+VlGyr6xwt59C5IZfjNbN53oefWIOVjg47dwLNeYocHBwcHBwcHOBeihwcHBwcHBwcACySPjPGwI9oBKW7khSXyJzb6rrvrnzxurmxuypgTo47pKom3UnOLYyHL3JwJTNUUR0oXSPlygp9li/wPJ3yKK2nKi0bdL9nk7gpVat1v79+wACIPa+mOwWU5L70YJEtF8dlAYLb+AAAIABJREFUe5cL9YxTdDYQJVKCcu1xIqVhRInmiRs3K+7jZlNisEQuWF8Ud77X/duhLS7lsriamw2V4dPXrJRdv2Ctja9jEzRsj9ge3eTrODlcRNA5uZyv+/WTbdKd0kyow5Ruk3r2U/whI9JcjXSh7WZVkq/9+JSSnEyhK53dXTGrc0BdaNL9+0ParNEQmc4FRKtRw+zBnQCAA9tJAR09QrsjPQaAvNSZIeuE4Bjptqs2hzTRtZs3x9s0zMQtx3nuf/7hw7F99dU3x3Y7wxA+yyap2lo1Sjrs+muuje2GqnnTVLpdcv0rY3vrN78c2ztW3Bnbm9eRxl+XCqm6Zw7uibf9wyc+Hds/8443x/amtSzj2K2viO1KWpRWMk+s33Q5+g4LdJTtSg/XE7SPyN01FIrQ36p29KPJu5diWqk0HdSDgyti+5U3kYL05vfG9kOH2W9aCSpPQn3IRKDLV5T6a1j2UV/Cn3SWPFTlHGlReBt5Dgci98/omhbp86NC93bienmLeHw6T5GDg4ODg4ODA9xLkYODg4ODg4MDgMWqzwxgosiu6rZWpY9KsTSwWjJgoa4u7/zc3bWekkiyOV9CpYvrtyllCcQ9OTDOQISX3Uo38L6tjNI6e+BAbEs2j4TyKZWlCqndprIq5YXlSUsQLj2HJ/esbkXbI+ibUhMaSbRfiGmThHyuO31gNZCiRA1PRBbvmInuoJKJHnRiYmt3ys62SZmoQsiTAGBKCSUjJosLWoOkiY+1FQV4tJA+JtHOkVDcMdBdSyQZWhdBgj7rT5A/RRBYlKN0CKZH2pYkU92dEuu6Sw+ZWS+VWa8gnWmhMTMpjYrO8ZWR9kxQXNLmbSvjUcZJrc4+UmvVo6ILvZYY31KwRCBJKXoiyCt/WCjXTtl2QZHKwi7ZAABYsZ7KqoUqlWATU+yDpToLumsXI/nuPMho2FffGQYvbIPz5vEq6Y3Lr7gqtm88Vo7teVGNrZxnUL3DQnvtOkgq7aobOM/u2vF8bEsGJdSPkHJe1eK16hXuZG99b2w/+/XPAgCWXXNJvG3vEdbFkR0MNnlgL/vVmqupfltXYt9QBIvhWRaDaP5Q5XFd+11GAzayHlpCQQWJCbEjA+9+OaWkczIvLk9x7BSbpH/XSuqTbZI3plqV5QRCnykV3tJUSzJPtuQZnZLlKx160JfguLrcAeiunFNGsFRm2QNf5uBorl+Mett5ihwcHBwcHBwc4F6KHBwcHBwcHBwALFZ95qWQLoxHtiZ+FQrDV1vos4S3uosbsK0yMNoaSGw4xePGxR87vIwBw0ZXU02w6ToqIdbfwASGe7Zwdf3OJxjgbOnYQ7E9OECX3IK4CgsFoUIiasjX1e8tpcmEYlRlVCJ/miqsJHBh9/y4FxSd0gUJykzpEQnillD29HI5n+qyVEorwU0krtOdvwkkMeGs5GNqNklxjaxnu2bydA0H4tKtVeii17hgGaFz0MlhJu7llAQo0zvLC6VbGOI5WjVSGrVZJglGj8SmFxod2krpbFVsqjolMQR7la+ze4/Aq91yGIbloF2Q/HIFocYyeiIJNulVSdFoDjf0uA/IfFMQhUs9F6qNyg32g4ZkAq9LED3bk9bV+upOJfcDrWYFx44/BgA4VqZqKzPI/F2vfj2DDu6rkRo6WGUfvOZlDNTYOhb2x28e5nIBbxXnzRtGeZ2po8djuypJYBtbOD9WJQ8aZqnGPPLkltguXbSEZW9zXF28gcEmr15Ctdh9T5JWW3ITy7YtqoM7brop3rbl2Ldje9NVV7OMQit6dV6zUmY/mZc5eiTVf/+ABfPrJYloyVUm1G9WlFh6gC/zcWeM63MlkMlNabrRkeWxvcGwfp75wodjO5XlspANK6lQm63uZwFU0CbLA2yqO82tz4+MBPjMdJ7j8rxryHjUySQvVJ4uU9H3hZLkduvQZ+1FMNvOU+Tg4ODg4ODgAPdS5ODg4ODg4OAAYJH0WTY3iks33w0A8NKyWj5DmiGTE3VWlsHXEoH1uqlA2qIoqtCVWGhRwXDdTati+5bLNsX22FVXxvZsgUG6rNAfSnGt2kj34KveQlfuqPfW2B4u8D5KJbrtJgaZL6ZDrzQ12FZCWaa52rg1k2a5Uv5gbJsUXdgmw+1H9z6DCw3P81CMKIZEUEyl8xI0gdIK3YNcxvcr1Ijp4cZMBBjsEU2zOXcstssH6MZtW9ZfbpDBI9Pi9lV6N5uXPpFQ+KkixYvKIq5gdRFrAMOE6kgCNqqaRChlzanXLxgjDJOUVVUigeYL7EETKTr3rD1aYqkhJefLCI01KDmKMjLVBCWhMdukXGxTytuLmlX6XVV+uosoHdNRzrDhFMdxUKR9Qmi6oMY2TNC3vVR33Ut4wZBOe1i1IixrdfvueHt9GWmi1Eqhpg6Qqj28l7TWRdeQPhuI6OROfjwAODxDKm3rcY61g7t5zdXrGURx/6PMPRZoDq8Saa8HPvfJ2L7r1/99bOfGOQaHJQ/ZSJqUWOXBv4jtb3yXVF1pLmyfY1LG9VevjW1f6JMly0gxNhvM1fbdHY/y2MveGdtKl/cLBhZ+1LE0MKMvqkudaxoSnFRnYE/mlHY0aaaNqDU9ndxoDg8KxTzNIJ0NCe5Z9Tk21q7gs3V+GcfG7mOsfyMPNF9zn8mc4MszP62rKKLckTUZ9y05x1CeqvElQ1TFJfJPVrm04ogoC0eKQ9G1z33OdZ4iBwcHBwcHBwe4lyIHBwcHBwcHBwCLpM+sDdCMFByB5P2pltXBTleq7aE88sUHbyNmamyE7rtXbHhZbN99+22xnbuRrtEZQxf9A+IqfEL86ZP00MM2JBijqA+GxPV6Y3FpbN8sVMhAnq64n3vbq2K7PhW6Z+994B/jbSemZ2I769H154uiICeKnGyaeYOsYR207eLia54tjDFxmQKl+WQfpfyMqm8SJzr12EQWLHHpap65RABPDbTYZKOVjtK936pyeyCUVVlcwPlRBrrLDrCOE3l3tBC+lj1Sc6RI+aaFcg0kclirxb7flu1B0F1B4fkvzDcIxWKs25QEoEyk4pM/bNC9bakkkbErt6JqsmKGLu+01EO7zACCRoJYqmrGy5FG8ITyNqLySwjEpM6tBKCD2rXoWobqKa/Ncy8pcGzOSEDDekPVb90Dc9o+f1MGAVCthtdbPcz54StPfC+2ZwMGSbxklHV/pMV7KUrgv127QvVmaikpZk+C0k4Ixbxy7XpeZ/uTsT1/jKo0ox1B7MZRBsZ96r57WMZbqJZrtUmjXr6ZVM11l22I7X/48udjuxI9JLw9zH227Coed+Iot8+UOb5vvumK2L50CZcjnDg+GdtL1rI++gXPeChES0w0kGtN5tS00P0J5ZSoaJW17+Q+0wDBgQySbJrz30XD7B+tE6TPjASeTTU4TlLTpE8vH6VyrVJmux2blXHVc8lF91yLserMdleZZzUgryxHqcr4btQ5B6wYYxt2gr86+szBwcHBwcHBYZFYlBui3a5jbjb8Emi15Ms90Jgf3N8m4qVweybLr7FVV4Xen3e+8e3xtldcyQWF9RX8gvmILMzaVeJbZlUWaS80NAsx7YxmCpav1Irh19I9k7yPrzVoTwxzn7ctoTfi/f/mFwEAt1zBUPh/+rV/ju3927bHtinLglxZ2NnKyWK0vLoXJN1EH2A8H7li+PXUaqvHpEf6lsQXiWZ917QM9uRdT1q4KqHZbfeFq81ZiZFygrZ6MzxZUNuYORrblRNciJ8tciGmhp7XUPkaTyf+wpHFfPpl1tLV4LLg0dcVhG3en8Y4ysnCwb7B0pvnJ/NZiK2xwLqfJhlvqBOOn1vzsjh0UAQWvqRhaZW5CNKTus8W6IXLj9ADUhhjrBpf6spTT5HekniK6iXOJdV5fr1WpkJvQCALMgPxNmoGlyWD9NDO6lpOWYytntLOYtl+LbhuNZqY2h8unrbLmNriztdR5DFdWYjtx7d8h2Uz9IA1pumlW94Ox/qzj3Gx9Oa7mF2+un9nbJcPUdQw9ezTsa0Z23Pj9KrnBtmupUmO2QXxMu3OcmycmKKnJi2ClhWyAHv9CD0dO+fDvrVW5uFhEdSMykBui0doxQj71cw4BRnz07z+ky32j37BgJ6LdiLre/dFyU3xiGjKHA+net01xYa6OvIDXIg/BrZPo8YxkJbxaAN2/KrEuhofYT1fu5Reox/LM+zEfInnYRHgyT3VdPG41/mfBR7IkklRUct0iedW4UN+hKKk8UEee3x2Jtr33FMrOU+Rg4ODg4ODgwPcS5GDg4ODg4ODA4BFL7S2aLRDt1m7pbE91Bevi650YSX3uPZ6Lqh7378NY0aMrWdMja2SqfxhWXW2rU57XhgcDQ2kiyBlfS2WCH92Nz2zyIt78tEGq+WJo9z/+HHa5RTv9e1Dodvu1Xe+Md7myQLWPzryV7F98AAXBCcytcui4YyU0Rq6LfsBG7TRjLJuN2SBt1Ketgd9potOTSLL9KlpJmwiUBHbJnGYLK4uH93HzZL+ITNEd3A6w/PU5kgRVI7x2MJY90XXJpHavUu8JU3TIq5uPS4RGyfpL5YfaDYlvUT/YOP20oWaSme3hQLstVjeS9CL4U3IGkgMSOwxXxaNtmURpi/nyI6M8NilpJkLS0ifQVIAWOkYib6ocaKUgpDypIfpUs8PhfbsQcbtaZUogmjXhUqThcLDRdIvLaHpmvXusWP6gbTvYWIkpBE3XXtDvH0hw2z0swe4GHbZwMWxfe0V1/FEI6TSJlZeBAAYFwp7rk66MZ8XCnGSi3FnDkg6D6GNRzbzOsUhdpDWQz+M7eYs49o0DnP81ots+8NHSdUNjQqNKmknTBDSmMvWkBJv7We5jo3yPpddQjGO0jOrZTHufT9+nPexmn2yXwgAVKOHVKCpn6DUGOswp6k7ZAxof4znWhm7ubzE01shop0F6fcBn63DF10V26NCjR148vuxPT/Fel66+tLY3jTBNry/RLot0JQbGtNMxlg2SguiNGGgMYsk631DUoVMDPOZWBBaryS0+VT0PGi1ta7ODs5T5ODg4ODg4OAA91Lk4ODg4ODg4ABgkfQZYGPazIp7KqkyE/egrAAvpmi//nK6Xq9aF8bbeAB0/T1Z474T8vp2hYQrf9DjPgeEPtNQJTmJw/COIt1wrx4Q97ucPy0sx9457lNv0N4ncUDumQnPb0d4klvueF1sX7OdbssjX/r72DY1qh8CS7d8vcmV9r3SY1woGGPgRa7JRNbx5F6J/WPb0/gxSr2EbZ8ousiGlF3S3B4NUZzVphia3/ikRobWXR7b+SzbdWr7UzzPPM9TOk5VWqZAutakNPO5ys9OjZ0RWIlvJEXXOEBJGkr/ELVS69yVEOcKC5NIf8HtGg+KDaAUV9Aj230n5caAKM4yqsISykwzDOSHSUENrGC287TEkWoItR7UqfJKSTwvT7JiNzUNQpVjptHgoNW4QoNRCpuRNWz76YNyz3MST03Op9fMyX036hr0DH1FNj+IS668AwBw6SWkfebaoswLSBnsenpHbJ+okk5eMcR7eWJvGPPrilsZ36g0yfnmgS0PcPs+UnNtobDTy0lfDV10GbdnJUXDWsYWm93OFE0Lh0jJXXE7VW9rLyaFY0ZY3699K9XI2//6rwEAx2aouFsmlBCWXRSbDSlvWRRSmSzpFn+Q9qWrqOjrJ/xI1ZqV+DlNyfSuKrKUZJ3P6BiQh1slitPTkj4/kiFFuVZUsY15UftlSHstWbs5todXk7osS91OPcP0KAsH9sb2quWst0tWsVzP7mXMKGNlLHm6rCb8vy3vEDNNzjzDo+wHKwZJjaZFjVoSxdtMmfNHsRDOPZ6LU+Tg4ODg4ODgsDi4lyIHBwcHBwcHB5xHmo84DUMiOFL3lA1Nn+7B1ZvpBrvlDrrttkRBCn9UkUCLEjXvzXkWdal4xFYP8r3uu7LQ/Ck5T1rTF8gdSyJ0DEt5r8/wj9IqrtL/tATlOzHPfZ5vhSf6zIwEklzJC/3ab7wjtts1nu/eL/9lbDdbQkHUeR7P9oiud4EQBAEqtdDtaHoEF0ykNlCKLREQsEvb2+776vmCBoOmlY/sje2aUE3+EgZjzC+lqiSTYoMXJ5ghvCkKv4qo2PJLGLgtP0JqRznKjmJOVQupBBul9yR0k0QBNEKtauh7z3thvkE6TZEI8tYjxKAqrhKxNqXbpSOXfyFNGhMS0NCToK2+pIzIj5PyaeRIVRw6KoE5Jf1H2uNFx8YYFHB4jO3WkOnm6HEeW5om3dpqky7J58PyLJ+gqmZEqLyputBuVbriIXRcoUjlXFmyqbdlPugH8vkcrr4yVJR5QqUsHGDdN6dITYwIhRIssO3XX8P73b0jpJm/+xAVYak6++vB7QzSWD4sijOhb0YuuTK2M6OkavQTe3jTNbE9f4BUWrskKtF9W2K7Osb576FJqpjWLOczwkQT9oPf/ka8zZcghMNlCa47yjRMTx8khb55iPNvKssCr1m3Di8EOqIsXW6gSd+zvdIeyXhMCSVUiNZ9yCMDK0Y57pbUJIixBE5MreT8t6/KsbP1UVKm6wd4nrGVpMmq+/ay7EdJP189zjF2YoxjaW6BbVsS9WYj6q8aYHFwgMrRbIL64j6FDFWqVRmnA0Ye7n7HPvfQqs5T5ODg4ODg4OAA91Lk4ODg4ODg4ABgseozayXPmeZhEcpDKImRImmZ17ySioPWhmtj+54oh9k2ob3uKtD1NSG0jZI8t2iWbXp4URdP//OS0+tLFclm7vP275DcY0vF43abXOz+Fdx/WpRxthqe/0CT1/nGHK/z+xcxgNZdb6ab9qvfl7xmFboB05qaKugvfWZtgEYjdGnmCz2osUTeHdlFRVuJXHedwzTonl6Tdac5zipTDEpnpDHzQqVAMicjzfrLL2e96nmqJSou5o+SVksVmEvKl34TRH7qdqAKLUIDUhqhw2xCoeeLLfvYPsuVEDqL4zxJPdRkCWgA0R50aCoKkpiS+1IlnSoSMyPs675QKwclF1ZllqqWQo4DrCEcwKFDpFx8yYdUEcZqdpbtvGKUFJuXIzVw9HhIE03OsB9cuo5BDnOjVOFUaqRyjaiXsgUJHprR3FD9pc98AxQi9VBOrrtpLe913yHSXbv2sY43XbU+tg8fYj4zPxdSTAs7no23TR4iXdXaSQVbW9R4mXFScKOXMDO95qXTcKbeBMfj8KXcv/L81ti+cpQKpWtGqSi7acMrY7sxTqru6W+FfeXRXdu47+2vYXknOb6n50gP5g+S4qlceX1sj2RJ98y3+jvPduBH84uqKFUt2k4kidQgq9zu65wSLQ3Ji1pzLEV6FbOkKzMydw6vowL0oQNskwe3cNyVrn5ZbL/lqptiuyUUaFPGdWaO8/GN41Qofr/K/rdQFWo7mr+zkjsxLQFZdQlD0OBYq4L99fgc+81wljR3oxkeu5g513mKHBwcHBwcHBzgXoocHBwcHBwcHAAsVn0GDbgkLtNE8Ebuv3GAXNZdFzP43rMtuvwOlsPz1YXqOlbjSQ6nee4NQvPoDVwn1/Sz/OMecfvvFVXGd2foklsywjO9Ni9Bs8T7NlynyzMvVGElolo8uemFOdbL4SnayzYxb8y6a98S27UjVAD4op5JxlO8FxceRi6S4E9iK+jVxufwTq1105YgfSXJedQSaVFujIqz4XHSZ7ZFtUlDpIT+AN3BOVEa1fbvje3qJF3DVVGi5Ubpdu24W22gQUmFXzPd1Vomob7rHtA0ESSyj+hQfEmloI7N7rnZkvfDNu943VsB616Dtvriuk8NUT1SFWq7VKICZWmBSrTlq9bH9nSJdM2+/c/F9oIEh2wHHMtpCRC7dClpMDNA2q7aCs9ZmmbQQL3RrASFqx4jFeC1a2ILjSRcajw99atZPQMvG1Z+IO1R9Tg/HJ+nsmpU1HtHtj8c2zsq3OdwpBqszJF6zJc5D84fpYoTMm8ObKQKLLuE9RsIjTo9T1pjcIj1OnwxKbCy5Gp7/Bm2ycoNpFDGVrFvja3lPPAzrw3P8+6X8ffVL391bC80eM0DT3KuHJynim5u/pnYnjU899ErGdyznzAdJbQGP5W2bSvlrXn+rFBJ3qnBZFdLvri1BaG5Jxnc0x9icMvhNaTGBqfvj+18Y29sH5visVjN9l9zM+mzPT/4/9h773hLrrNKdO2qk28OndXqVrJkWZZlWzIOwtngAMaAx8bPD/AjDjziwPB4b3hgZhhmgAGG8MDMEAy/MeHZBmMbG2PLlq1oS7JyK0utbnW8OZ1cteePvU+tVd3ndN++3bclefb6/fTT13XrVO3aqXZ9a6/vu5H3WiN9ecEI6dMXiypt3wxpzcRvkSjIPCFxLGEleWNd1K7zCxJdOWK/KckWmOqQU6jFG1D8Bk9RQEBAQEBAQADCoiggICAgICAgAMCGc58hcxnbVBQY4m6NxcUXTzDYUvMSutVu7dLdNecDIHaEPrtPdqLLZn38pLjYNCeaaLlwtbi0t8kfbhxmub62QPfcvkN0yV0tP5gc4g3eJ0EdMcHr3NSLyNWVAGspKYXPUtSC9+ylm/O7r/+uzP6XrzF3VwK6Cq3d3HxZ1lqk3k2ZiAJDd+1rPqtcEEBljPpQNaqqUFdwe4Hu/IYoxSDBAYd27M3s0hDVRKnkhWs0SavEsSgYJhl0rDgrgQJXqXpaPUZXb1HUkZGPpGaM9OucgkFzpml+P6k7cftq0LXN154BMP1z2OnXTyQyMz3eld8VxO2c5RrqSnRUCaYWlVl/lSGqzxpyDaUFOtLPNGdZRwK7qcKmMODbLdcsQmtFEduu4IPbKXWZKvVXk7aXAIW2yzGouZlQYh89H2Ror6bqLfb7owuk2vdMMEji6EsZvPC+R+7O7JkjpI1X6q6Ox8ZJc3YOcywkkj+qME7aevIybntQFviJfbzPbfcyGOMLX/LqzH7ZDjbO1dds43VmSdV94lOk1S5pkmJ5807OD3Hi6Llja6Q2V77OXG3Tr3pfZk8U2JeKXV6vfYRKtErK+889TsXbpsK/04zMEUmqNBlPFdI+N+/q+cYrtyZGuX0gnWeddeSdNLaHFOHYDqrDLp1j/dxZpZpxpcX5dVnKu/1S1tW2JZ5z5M5bM3tV5t1rdlG52I7ZLo/Ouva0RY7BkVHO9WUZ0yuLbLem4Ri8/GJSgnu2k9ZdWHXjpVjSWlwfgqcoICAgICAgIABhURQQEBAQEBAQAGCD9FkUGdT87u7GmrjRNReUUGljk3R3RUXdUU7XedeTX+r6b3R47rFV3mdFksVMVnm+sG05Km2nHH+TqNKWhEq7e1WCYx2lC/n79tC1d+EYz79Icl3d6wNPLq/xWDcR5ZwEZmwLZbh7lwSbAl3+bQlUlZ4HzqUXIywX6EryOmmgvkKsyof+arWeZTXnXItBtlaOMB9Zp0NqojTJvGZVCaqnFG1LclU125LLJ+Z1NPBjYZxuZbNGpVtjloqUiig3hia8gkUok6TL+0fy/EaDq8k5zQZdym15vjQ5PwHiMlFUTnEmf++Xpw5AkvSn1Ywf10midKgEtxSOsKT9QwL7jYsa6egh1v382iM8XwNGCgU2LMEbV0R4kqPPlFLQsscnX1v7bSSJ7WKxE72IXDuSoJ5p2ttDgE1Bt5Ni4bjj3pOYfap5hOqfx+dIJb3sJS/K7Ifu+XJmj+5h8MTdBddWS/s5FuafIX1mVXF2MX9XnSY1cfQgAzzecsuNmT07S3q6sUz7ytdfktlXfNPlmf3IV9nGB+4lxVe7nWqop6Y5fu9/1NGGdx5k41w09PXMHtlHCm5LkfVywS4qp+YOPprZ8y3WY7QqNP4mwcJmQRsj2V6S9p9GczRZLjisqIGnvWp6qsbnTeb4XEYCGk4IBVoY4paWLds4/w2P8F0925F5LOVca4Z5zakXvornLFJ9OP/gPZm9cpx94bKt3D6zZF2brwktv2WUY70tNOmCbKsZm2SfKFR4fK3O+ze8kjkEbwwICAgICAgI2CDCoiggICAgICAgABukz+I4wui4c7MZCaCGlOqHoRLt7dtIhRSLogKpn0wn5DziYs/Se4d/pFcN38Hb4CJNUqXlFVvzmr11WFzhW3jW4pLmpSGUghgf5j9GPcNWr9u+J3e6PN4RUdNlu3ifneM8f77EwIIdex7WrcaVI2nTXTp/lBRXIiqE8XGWrRhrEEBVn7nrdUQd1pqj0mN1ge7dpEA3bnGSypROm5K9ZQkKNzND5VpLREFDEoSvVpFOUWagQNR4r25dlBXH6Lo3Ra/KMNIHxL1bEEpI6bP2Ksu4LIHx1taoGkqSzVUSAo468umyYAbkVMp7lEVNJ0c1517PBZ1XsCkfp7S5uvn78wIFycE0PDIm59B1vrpMRczaMuswLZHOztO9Wreaty0+4QgkbyOASHmy/u0z4FHzistNQH1tGXff+nkAQCRj8NgS66MDqnUWwDHQNazL8a1UY1ZWXb0uzXA8pqu8XiyBL3OKs4h188wTzJU1P0dVkCotVxd4/Kb9VD2VL6FaqFvipL5WJ513962kuBYKvFfLT9Gz4PvkLa96RWbfdfMXMnvPm5hj856jpG9GpjjPt1c4Zu/86hex2YhgUCr4jR2qrpS5pil0e6L0twy+VP6xtey2HIyscMtHV7ZfDF9A5dfQzt08R7eajLEPjU5TPdo9wjm4K4rRRFSl5S0MgLn9RWyLxjGO39YM8w4W5knPXTbl+sKS4daKiQKff/883xOpqD63jnJOL8q2k0ad9VguuOcwG9CIBk9RQEBAQEBAQADCoiggICAgICAgAMBGc5/ZDjot5x4bm6Qrs1Cmemiswnwn27aTcilIkL0oVjWLd9Gre1r+0YxY1C93JO/YMl2Mu8St94Ia7WvlKTX+4qViv0OUZTNl0i+VATWk5Yw9yRalPBhJeVUVp79bXaLbcO7AZzP7eJP8YL1Lt+FmwBg3pxImAAAgAElEQVTSYEaD+hUkeJ/QHbFSZlF/qqQX1LBbl2CJx0XhIuxFRSizUo31fvzoE5ndiYRik7KvieKrLPW9JAEhC1X+dniSweiirtAHK3T1J3XXb0sj7KeJKPEKVbqXC9oHpF4KZf42Fnq505IonpuI3jAQMRlSUYYmOVZLFWrSntJpe926KH0ilfZW0qktddUS2nFZcp9tGaa7fueFezO7IcquA/spM1teJTVQmVBdKdGSvIRxIsoefzinPhO7K2rGjuTe07qIY1UKCUe0ybnPTKeN4nGXH+y+Z0gbp2C9rkwyCN9Mi3PF9C4ef9M3M9jewze7nGBPHeR4NNI3hvcyN2NtB+dzm5vviCiXw4vHU/nHvOSbXEtYxuGdVKIN73o8s+ceeCizH7nl9sweuuIyZ2zlnLH3FW/O7C/ewXx5i5NUnA3HnA/mljkGJ68ltWS+fCM2HcYg9nNmV9St6pkoacDiqL/S10iQ250+b2N05KnsWFfmwtFLqPwrDJF20nxrqPL4th2kWqNDVBlqYNWudIZIthOUL2Tf2fGSl2f20zd/nvddZD/eVnRjeUi21xxaYL9clXxnY1Psi7umuJ5oJZITrcO2rfh9KkF9FhAQEBAQEBCwQYRFUUBAQEBAQEAANkqfJSnaXrEwMUaabFiC4G3f9rLMLo/QXQ5TFZtmL3BaTjGTyxtFN1hLzrlVqBilZV4sga/qMS/0asllVhNX5SVSlkuq/deKInrLBYcs9FzFojIzEtxRc7iUpMYPH6O7fmlOVEoS+KwUk/LZDFhLZUFFAnqNjNNFPTZG92as+aFyAcWI2FMPBXER24ZQR0XSZLVp9p9Oi889e5QB/ooTDPg1KqrGsgbba9ONujRHF21hlK7Wyd1UX0Ryfn2eKpTWoqOFC1W6kQtyT6XJchRLJDm0hFYsFYt9z98sWABt3/c1KJzmoesMCCJZyPEikrfMu8ujqH/b2zrrsiVKwbjMcR8p3aaBMTt0y6cSoE0DdhaLMmZFWZjGHEyHRakyIhLPVR9Is1TmuSWh8BsLpFa6GqRT1UGieEvl2tEmf1KuJgluXnL1uXWEc8W+g6R73/hK0kfXf9PVmf3P/8yx9PRBjqWn73W5ypoSaC+qUbU2/gLSTmtC967MSvDVNdZBLhei6U9V1EXl9eTjpGQqI2zLqYuodCsepBq0KXm52kfdPDQsubTueGY5s1/42m/N7H2PkH5//Wuvz+wDt9+Z2QfvJ2W3siKy4M2CtbA95aOoO9MBOQeTAZTvNtkSUFtzdWXrrIfq1N7MHruIFKWVa8vtEUvusb17hNK8fz/LIvSZ8qRW2jxRVelVfP9Pr7K/Hvr6VzO7s+gUh6PckYCZjszLPIw9I6T4ykLZpTH77miTD/WEV0+3uxLtdZ0InqKAgICAgICAAIRFUUBAQEBAQEAAgA3SZwDdb7EEdaovzmb2oTaVAKMx3e6vejPds2MVur7ilrtOIi70QUHh8jIHHh8SV+5si+W6WRRAF8iu+4uGWa7ygMCPCiU/hATEkKdUSsKpiccfF1YkD5xQK5fsotvy2976lsw+PkdXddcyuN0jt3zk9IU8Y9hMUZNT6BjtGhqYURRqA+ig1Ockq0ugxVTy2BVG+UxGAipCcpkNjTCIXLvB4F/H5yVImdBAzQrrclQUbbGoxYo13rc0TXqstSZ5pVbmegXIjtWmqPZINceWKvFUfWcG9OHNkimdgF5wN4v+7nctkwZpbGuQUQlbWoydC3pY6FUrNGraIbHcFkXl2BSvNyFBGo8dI52z/CTplMhqXjm28+Q2Bogbkuts38EyHjrMay6tkBKrVdyg3LGVNHQqcrn2ouS/Ev49kedryVhoi3vf+HGzWa3abrVw8HGXG27bC6giqg6Jymw3qe1ykfVXEgXt5z/3ycxuft3RZ5rnbugCXruyjXTzXXcxf9oDD4oSSXL7ddeRnHHpOAPB3voljuWKqEHf8IY3ZvboZaRw2nfexbIfcUq86kHmvvrKzSzj+97+tsx+7Cs3swCveWVmbgf7xm33kkp7ywuvPe1znDUMAK9k1JmjKf0xkvosyPjtClW8dyvpw3LDqbV0a8ekqMAqMs8ZoZ16AXYBwKQ8vm2K2xmmtrKeU+lbmnvNihJNFYqQd/v4lddl9orQ63MPucCczZlDvP823n8ukWCuEgT3cQkK3JH3/0qd74/ZFXdOdwP5JoOnKCAgICAgICAAYVEUEBAQEBAQEABgo+ozGNjUudNSyfnUSuliOyYU0GSLFEb9PtJql0remmc8xTUn6zQNemjF3StpeFAQauwSyWX2YrEn5PwFid44LUtCyZY1EIPosx47ZiWy4FBEV/wbx1kvQ8fp5r/v1v8/sx+7/5HMXjxCGrJTIC20abC5/3lb6SClMUUhNEAd0VpwtFlTqQlROAxNk96C5K6JYrbCuJyztkSXe2NV8qZJAp+owj42PEpKIdUAk6KeKou6rrrA67cXnGqntUL1TnmYrmCrsTTl2jkVjtiqYtp0uRJcGzIwW38lp0Jp6STtf1Kj5dqoXZEgliUJLCeqz+YyXdsVoU93TdItXpY8RisSOLMoSpaJUVIrY5N040dCa23fznNGx0TCIgqxij/fSP9cOc7And065ylVDVqhqFZEldYWO/UUhMXpKaSNoFIq4QV7nPJyRgLZveqNb8rsKVBd88nPfC6zH32I82x6gEqs9qzr16bM+hq//MWZXZYAf5dedkVmT0v7zezn9e568MHMbnU1jKcEhBwlFf6q1zKQ5OQU23VCVKiF0jWZvXZgP+2jjvpKD7PPXHIt9yx8+e//OrNvv/+WzH76w3zNvf7FpAq7LZJOaUVn9M1BBKDi+/iK9JlYVWEyUBPleasaUFjo3CVHGcVV0sqju1mXcYXX7krOPyvbSHTmGpsgffqmV79KjquCXOgzWUJYq8Em5frjLM/WK/jO78y7dlw7xPFYKrI/X76NARsfXeB8PC+qyFiUrwsN1tfoqOtbcXTmS5zgKQoICAgICAgIQFgUBQQEBAQEBAQA2CB9ZixQ9FvN26JEQFHyRUlwvAfmuSv8s0/cl9nveQ0Ddh0Zdu7c2xbFZSh0nFXVjzj8iuL7u0qYpreUVZXWX1q2HspMoW7Gmih1Kt41XxH3uwjLsEvyqn3xLgbZ+t1/YiCr5PBxuTrrqzo1hs2FQcHzlInUcT4wY/+gX5qbDi0+V+OoC8qVSo660hbSVcPbmZcJli7suUN0yx8+zBw4aUyX/vAYKZOOqNUKKa9zeD9zJxXHJAjlNFVM1QqvWdtCN21z1dEpzSWqVFYleF88RCqtIMHKIpFeqCDH5Kg0nBf0KDHNd6YUj5ZDz1FVmn4tdb0bf01yC01L/bVbdMunEiytPkO3eFHyh+0Wt3iyhf1b86lp7kAjOaC08AUp7+gw6aBYnqnbcH1keUYUb8c51ozkajPSzl3Jt7cklK1VCuKE/59rjIyO4XVv/nYAwL7H92XHX3o1aY3pMlvqo//0qcyuL3M8poc4lnrK3arknBvdw6CmRubwLbupYpreyecuJhx39z7EsdaWLHg5Kr7EupzYdXFm75qWuU1+YCepmJq64kWZ3Zx1irLmUarZRkukUz/w4z+W2Vddujez//DDf5nZByUX2OsuphL6dq+E2kyYyKDo8yKmHb439d2mit6oxLbdu419s9YiLY22678je1ivQxfszexUg0EqHWdkTMmkHsl81e1yTn3wSW7vsJfxzblTAvumiV6fppWosDVp/50vcqrAQyv/kh1rzPLZygVSdrtlC8OTwtcvJVSlbZEokKOTLidcsbAOWfkJCJ6igICAgICAgACERVFAQEBAQEBAAIANB2+02e71FQneWJT8KGVxW8UR3XDHn/oaL/Mo6bPLLn01AOCpGtdps00JXiV3l83vueBNd6/x/GlRgl0vHjSlzDSenWa+GeQO1ywqRxPed9GX4aIiC/ZdIxJI7XEGp7rrE/dmdlpnrrixSbr+ul1xfbc1LNfmwHpXakFzF9m8Fq0HYyTolygYWnOkJ3qqM1OUHD0SgC+SSJlG5FzVYQmYWGNLRVUJUCeqvgVRapREPdIRdYIKvrpCt6Ul0j9FCRRZ9Xn6kgYplq4opLptBk5DRaVoOc6MphRAg55tJozt5RFkmYpCO8VCR3XFda7e7xxP6p9hrcURUC1Iu0leou4K26e9xnZYEAonafM6ta1Uu5iqKlxU2SgFk3FnlPoTxVl9iYrY1XlHgzbmqTCMWqT2rfTRVlkUZ02ek0quNo1Xak74/7lGN02w2HTlXxAV6uEF1sfxZSpV9+wiNXTgANVXSwuiAvVU1vgVVJzFw2y/vJKOfTcSuqUglJURWrQ2LPn/hOLQ+usKXZnn4oXqldx0I5eRPqs95XKVrTzNvvTY56m4W/3Wb8ns172JedAefvjRzL79Kzdl9quue2lmL9/8GWw2rDXoJq6+ioZ1qLQjRK0ayxy1s8T8j6UZbjNIPN05tpdUZzxGWtJqHFkZO6mR9pG339oCc83ddhPr9q5Z0lTfWeCY2fYi5tuLNQ+aVRWdlEGUp+MvcG3bXiZldvAOrg+SWeamG4tI8W6V/lQY4ntzeoJbJZKSK0u0gT0LwVMUEBAQEBAQEICwKAoICAgICAgIALBR9ZkxKHgXvJUIiwWJqliTPGDbL6Y7b4/ktbnrjoOZfVHRUUbX7+K5XxKv4qLwZ+p1TcQ/uF/os8+Jz+6ASNRiUY0lXaWFxLUolKAGptM8PweEtltrueMvmWBZLhrlD//y45/O7Bu+zPxl3SVSYwvibrSiEshRB5uEnuqspAe1PDlpiLi5W1RQrB0TesTXfUlyG5VGSY0kXSUriaFpqtIurPH85grpxJnDT2Z2q0EaJufGH2NQuEKBT1VfIMWX1OkCVjopGnJUXVyWIJHynO0lodJqdN1G6E8XdORZ88HtNgcGDFSowU/Fc462lE8Dx1Vida9rIDZ3vZYEUJ1ZpTt9hyi/KiO0m8s8Bw329UVRFtalPstCYxZrdLObqtgy9rtNtn97lW3UWGagt3aPSpUxFZVIzXZFGbUm/XxN1EFJjj4TSsnXnbWbQ6CtrNbxpVsc3f7ab3t7dnxXhXU5s8SyjZnxzF59ghRLt81zyrsdxTa6lyqgXPGFNlflZO65ZUwNTXGMv+zlr8nsYovqza8/yjFru6pQy0XnlfsSRvrE1JVXAQAaoh6cf/juzP67P/mLzH7Bu74ts7/vh38os7d/9MOZvW8/A09uETXlZsFam819ulVBt4bE0sEnJ9lPhwyDjFqhdotbHJ0/vIf0ms3lOJMgiv1TNSKWP1SEjrpwG9u2LbnMtkoOSSN9y1huT4hFfd5TyAFA2mY7r/rAqVGR1zYFGZsrpMFjofUu3nVRZu8RVdrBJY775bq7T7KO3HwnIniKAgICAgICAgKw4Y3WBjaLI8J11doav9xWFmXzVodr/6OLjGuxtfpUZr+p7r7ornn7u1i4rfxKuLXFFd8x+aLVfcj6xXawwfOfFttIaohWi6voWDdkJfJVpJt55frbYh5/zZQ7/8VbuAq+8fEvZvZf3cQ4GYcl9cV4xI2Jkr0ANtUvtJz/5pzDgpnSdRuw1Q24GsdCvnDas/zi15QeKLqvrqpkc46L/btaLty8xKapjPArpVISj4w0U0Wy21fk66UgS/1E4uYk4g0sFlnhVs/xXiNT4xdIssxnay5wU2BLYhaVqmynbpMekqaID4zZ/G8QC5ttctQvwFg3skK9pbJpUbxtGoOq1/yayqXR4e+OrfArdnqEdTI0wTZpSfqPRLwFyQq/KJurFCRYja8ig0O1AKar7uMBMXL8/6MqPULtgqQdEldwvc2N4VY3GYsnUb3UaeY525xIRTZN0F51X79LT9OrMb+TXp57JX7RE1/knDP/JOdWK1/fY5c7b0ss3r38TWnmhAHS9hXZyHvtK1+X2ZdeKpt9U/b74hjn8eGKTHS5++oOdpmvZU4YutClHRndSy/Y/MPcjPvEzdwY/NI3vyGzx3Zeldlvfff7M/vOX/nVzD7U2XwvbmotVr0gIBLvo5VxWpBUR9MV1nMicdMK8n4Y2+6erTzBOlZPm76/rHitIxEmRLL5XeeD617MeFgvWuIcWFqmp25m9kBmN1fEuy5enu4q54d0TQQMPs1KKgKOjsY9lL5YX9Fr0C6P8h16fJ7epGN+zdHpqB9ufQieooCAgICAgIAAhEVRQEBAQEBAQACADdJnFgaJ/6mGKG+JS64hsUImDG+zukbK5Zi4SZ855Ny9ax3SE9e98Xsze/JipgZ4uMq13CPi/l5OJSOwZOxNhSZLO0IFWaWp+HylAss1JhvfLpEgR9cN8V7jHecGPPDQF7Jjf/Q/mLHZHqbL+JItdH3nQpDLJvVI4nQUxfX98L67cK5hYDNKTDOJK02lG3bRouuyfpR0h9ZrsZdhPGV/aC4ynoq64pU+y8eGl8NWaU7WzcSw0Crijm6Ju7bVJCUCcU2XhiWcvpQz9XSXyX0vyKbjBq+9cpyu48oUY2TE0paF+OSNuZsJa4GWpwKiqH+cIt1Am0hbdCRQUT6+Evz1NGUA77ksmyc7Mu63jHHzamWUtFokAgcrMaVSiV9kZFM3JK2EdsxcKC0Zs7qRGkXnXm/KAJ+X+EmtRO6ZExTItXMbjqXsG9jEeSbotJo48sRjAIAbj3Os3SBlqAxRVBA99VhmJy326fL2vZk9ttfZuXhjGkdGbeg4pT0xybl4fFLOl2si5py/Zy/jJ+lwT5sag03rXuZrq9S9G4djey7Lji1LzKLOLOvoq5/6bGZf94prMnvnhSzLd77jbZn9Wx/6Y2w2LCxSP9d0ZAB1ZO4q1rhZfkcs6YVW+JxW6nZyyjVAZ54pderH2L87IkBoL3NstkW8koq93BHxQp3XKa5xS4Btsd26cr5Sbyo2SYVztv2GmNDTxYrM0UK7lmUjvJX4cE/LO2hmgRut1+DeDbnUJutE8BQFBAQEBAQEBCAsigICAgICAgICAJxFnKKiV4R0NYS77G5viytteUUyAhfoblNXWn3JHf/LjzPT8xMP0x32nje9MrPf/Y63Zvb9E3Qx7hM67Hhb1FOSfiMtyk3F/R3L8fEyq+UyUU1dX5GYTIdIB/3FP7oQ8V98kmUvH+Uu+70TjKtQlKy+05N0Q84uSth3yxg9dtOSCBBMVyDuck3tIe7PXDoPccdqY3aXnVJi6WH5e7/8CCf8Lg8eV8WPFUWMxpYyOUqO56d6XD4BlsQFDVFdxb2bCZVUlGtobKuOKCwqGmNH0h0U5D6aKX7TYCneseKiT4XC1k8hJS8jzdYt9R/3ym30XKGxxNZYTEcXSTVWSpwPxmt0hQ+Ns68b+a2qZnLhVTSemNRtIlShKv5WvbKmLjGNVFlZEOVPLlaZilGlL2gm8NTX0WaRaDZJ0PZ9bGaF6qNh6WsTknto4bBkTxdoTJyFfS7ukY6jXFw0VSslqu5TW0xRLqVKVaRKM0o/1D6pSjelWNL+9FlPYZhIup5E2hVS3kNf+1Jm33jjN2f2i17OtBRPz7O+jKjlNgsFYzDl3y0rwlNGMetwy4jQ7XW+Y6xQjarIe+aOzwMAkrskHppIsrsSI0jntM6AeFGiIcaKbN3QLR3Q1F7yzh2W8RgX5XxRj1qhv4u99ECiDI3Gx+TvTO9UkNhLM3Nst8cPUIm4IpRxb7ozA98vgxE8RQEBAQEBAQEBCIuigICAgICAgAAAG6XPYBH5zL6ahiJVxYi463NZHXIh84WW8C682aP8+7/M3pDZd91zc2Z/80MMWPaaiy/N7He++jrec5huuNFRycRtxeWuKQNSUnzdJlMPTEuAxaqhguZz++nOvuF2V7blGdIpu7YzZcVome7Da3fQ9X3hBXQbfuJmBiFbNHRbJjl11mbAwPQCFuaCCwqVIAqdhgQy7Kp6T6ihXonNoLKvKy1Cf2WgyScBoDVA2RWp+1QDGOZs+W0xPunv+WzhPJpIduuuBoBMimILpbABV+4ZwwDGywV1PCpVEUm9qaIslZM01U3veFwQ17f8Li4KLSjjPpJ6U5XbclPSw3R4TrEoypMSr1MWClsDtKpariPqx0Tu22vakSFN6yLtKepAKyrDttRdW1IWtIW66Xi1rdkkitsYg6Kvh7Kk1qiWOSe1DjBIY6pUkqBxlCrJurcHM9gy7qF0qpZrEFUt1+xbkryKLVeKQVWo1/f/yJ+qqkpRQs8xhdSX/5ZK4Nm5RzK7vsQUIa+9jlscbrzp3Kt8AdfXaiOOLp4QGn5R5p8Xj1BZFc+SJurKXNoRKnNtyQVS1BCFBaNBWGVelnGalOQcyVxfrTKoZ2FsS2ZXJCjrscNUui0lHBurMZ+pIGOspJSZ0NzVmhvvozVREZdkfBf1/cFFxKooVluyTUdpV5PVUaDPAgICAgICAgI2hLAoCggICAgICAjARoM3Wouud0+pW08z3EYFUXjIcaVZcinoe+szoY5Sy130xxd57sf+hiqvGySVzt6Ltmf2yCQpsze+7nsye+sW5sF5dD9zuDx8hMG+yqtUWP3025iduvqGd2R2MxFXZNeVudNidbYsC7YoAa5ueYr0002PS0C5OqmARkpq7rygF7wxRxkp38Lnqu1k8LPSxE45/2RXuM1RYNGAU/u70POqO9v3uCqkci599Ke+7IAyKJKWcwc36lQJFqpUSEXiCk5E5WGKpGdSpaRUubbJwf4A14Y9tZg2Z2RylSu21ImMZaVtbdJTWWkgSv5MqbSKBFarSb3FJY4HnQIiCWAai8JFWK1cAEzNURjpheTxVKkSx8WT/m4GUaoyHc4tkOKblaCAGqyu2KeezymMgfXUYaksAV0lmmqrToUfpC6jAeOq1w9yyk3lhHO/G0BP6zgSZVEq53eFtilo1xNaNnd9PUcDdMq9sl9qEeU+2mcioV9Hp6h2nCiyLYfG+U4ZqjIg5ObBwPryjgqdO1qUHGeSuNEOc36xQitBxklG52reyK07MntZFGFKu2kuxI7S7AXZLiJBOockx9iUUG/TQskN1Tj2S2XW+dgw54FKh3Tb8SPuPdvqMngkIpax3OXzt5uyDUEos1KZ64WVNVEl+v9vZMYNnqKAgICAgICAAIRFUUBAQEBAQEAAgA3SZ4BQFOLeLJbErddUakwCRWlQL6HPSrFzvUUR3W7tjrgPRWmiirc5UbbNP7g/s180ciyzt++hQuPiV742s2+4jYqvG79wa2ZfXuZ9o9e8gbYsIWsVeW7/B83XdFx26BtRA+QiEYraJR0QfNBEm60+s0i9cipJ+ge4S2XtXBwhLVkYFvoqx0e536pSyGiwOGk/VQBG4gKOxO2bSm6yxCqVIneU/hHLb5W2s2aA8kXcyu01FzC0q8opObdQoQqjXKNbuCWKqpwKQoNQ5ujizYFNLTo+F5nSIloPSmGrOi7p9g+g13sC7aMFoXDKUt/jQ6yTnRfsyezSEMd1JJR3ZJQCVWpdgjFK/rpWg7Smjh8jqpbhEbmXV21ZDKIPld7k4SEJeicplXLnm00L25jdLOubiyuck+Iin2X6cgYjLF0iShzpv3GRFV6ruPpoi1qyKHXTEHWw0pmaO68u83Iiat621FmxRrpl5xgpjqKM8cWOjmWdI1mGhgTiTFJX5uPzDGpYqlEtdckePueVu3jtyWHmhzu8QLrx2JLQu5HmYdscWACtxKuspf6HIz7jkZbk7wTr0MYcp82m5h5z805VcoPVRMFWrfAaIxXmVRuqch5rytiZGKU6emhoa2Yvr3Lc1URNvXUrAyxqksxOxHYZ2bqb1+9ynlz1dNf8DOkzmwuOKkrxjvSDBu2KYR9NJeJq0u6p43HGCJ6igICAgICAgACERVFAQEBAQEBAAICNqs9AN5dSIbrCUndrV1Q60YAge0nqdqUnhi7Ybi7QI89NlZYRlYxeeVJc21czBhWqu+lOHJ+WQFlCy1Qk+F6UaG4kXqdcEaVH7NzDRnL/GKX7xA2tNEY+2JlSHZo3aHPXrRZkEzqiDFiTHGeokz/ItYM+gOZP8j5LqwqmVIN8ii3XqEiQzcntdLkWc8mv2Cc0HVMUSwAwdcUPDH7ZXzLUO1tVUUbKmCR03aYdCR7YoFu4I/3WajDDaMNs9bphYbO8RrlcXhJYTatTFXGJUrtCBfdoOCPjLvc7ic4aCVVeFsVMoaSKJQ2GKVSjXH9QjL+uNLpMPShLvkIr/Jw1PYWY0mf9owzmNIuxjlN5bqXbkh5NjE1BkqRYW3VjUmmnTkIK6JAEsmtrlFxpn6FR0kej/rHmlqhwrcmDL0kAyJLkrypIr1lu1+UczqFrDbbNlFTs3jH+9plZUl/pKOmZcoPviMVVjqWi0EIV/3zHjjKoYVvatVqj6mp6VMZawq0US6tC96V8F9TKypFuDqw16Kaub84nLMdKk3VVM3z2uUW2UUen2u7J78jtRdJVTaHmRoa3ZfYrXnZ9Zq/OzmT2fcf2Z/a8BOetN0kvrq5Sqd1d428X11hvaZXU2/iwULZTMq/IFpuO3xqiASMbLRlfMnfqVoC2lUCOor7UvH2UQIfgjQEBAQEBAQEBG0JYFAUEBAQEBAQEYMO5z6i8SJP+LlsriqFOp//OfnVsFbx7LMq5vvu70zX4mtUAfqpw0oBk4q7PB/6SYHWivOqKe9qK2iUXDE/oiN7SMpd/JpdsSpU8/d15qt7Kn7O5ChdrgY4PRFmUW7WW6C7tLA+ITpdrB6KnbtKAbxr0UG0jtJe1Qk0pTSIVn0hfioSWVBWT/javepOi5x6E1y9VnMvYpsxj11hlLjxr6c4vlmpiU2HTWqILulnnM9l0s5WEAGBQ8BRtLgBjmhtAmZmj0nJ5r/R0d36Uo45EtSZnq1LQyIjIjxeh1qVcrSbHndECyHMMS4C4Qsy2QMS+YC2pz27Tl0f6VkHaSoPe5W5pBow7zXqCppUAACAASURBVA+X2ZszRi2Atr+0UsJRKnn2ZNKx8g+lQlOhU47NujruyHwHyS03NUbKZH6RqiAjc+i4KAmbknOuKspDzWO3WhelW4FBC6NV0kP75zjeJsdI902VSdV1fKDCkuTEqlV5n/oq+8BTz/CcmVHO4fVUAgK22IerTfarTYOJEPutFm3pR+0u+6bpkD4zOWVof563d5lmm21/SCjKtsxXl0rAYaUXh1dVDcz6HJ4gHVYZ4vVrMdsnFZVheYzn75Dco1Vpo06dc2Oh6ub+iZiquMmE/aMgVHx9Sfp2LNcTuyhbWppeCWkHbJM4FYKnKCAgICAgICAAYVEUEBAQEBAQEAAAMHYDu7ONMTMAnj73xQk4DfZYa7ec/rT1I7Tls4Zz3pZAaM9nCaEtv7EQ2vMbB2fclhtaFAUEBAQEBAQEfKMh0GcBAQEBAQEBAQiLooCAgICAgIAAAM+xRZExZlXstxtjHjXG7DnVb87x/f/UGHPlac75sDHm3X2O7zXG/G+bV7rNgTEmMcbcY4y51xjzdWPMq5/tMq0HxpiyMeYLvuzvPeFv/94Y8+azuPZnjTEXnH0pNxfGmH9njHnQGHOfr4dv2uT73WiMufZUx40xnzHGjJ/864HXfKcx5he9/a5TjT9jzM8YY77P29cYY273z32nMeYV/rgxxvy+MeZxXy8v88dfb4yx/vwHjDGf6pXTGLPTGPOxdZS16ev8g8aYn5fj7zLGXGmMKRljvmKMWVeoE2PMTxljHjLGfGQ95282fB31Hf8n/m3QPHi+cao+Y4z5173+corfX2OMefvmlO7UOPHexpjv9f3zSWPMXcaY24wx3/lslM2X5wPGmD/cxOuf2KdO217nA8+pRVEPxpg3Afh9AG+z1p6XjWnGmNha+0PW2n0bvMReAM+7RRGAhrX2GmvtSwD83wD+0/m4qcmlRN8QXgoAvux/p3+w1v6ytfYLGyxXFcCUtfaZsyzfpsIY8yoA3wbgZdbaqwG8GcDBZ7dUgLX27dbaxdOfmZ3/SWvtf/b/fBeAQS+4AoAfAPDX/tBvAvhVa+01AH7Z/xsA3gbgMv/fjwD4Y7lM4vvLVQDmAfyfvgyHrbXrecHHAP65z/F3AbjSWtsGcAOA9/Y5px9+HMBbrLXvX+f5m43XAxj0UXSqvz2bGNhnrLUfstb+1Wl+fw2AM1oUrXfRuw5k9zYuD82PA/iQtfZia+3LAXwPgE39ODuHz7IRvB7Sp9bZXpsPa+1z5j8AqwBeC+BJAFfI8S0APg7gDv/fa/zxIQB/DuBrAO4G8B3++AcA/COAGwE8BuBXTnG/3wZwL4Dr/fnX+r/9IIBH/bX/O4A/9Mc/DLdgu9WX893++O0AlgDcA+Bnn+26PJM6F/tfAfiE/Pvf+vq+D+4F1Dv2U97+XQBf9PYbAXzE238M4E4AD/Z+54/vB/AbAL4ON+AvgXvJ3AXgJm1z+c0kgE/4MtwO4GoAWwE8LvV9yQm/+bC0y3W+re71bTkC93L7LXm2H5Xfvg3Ab3r7PwPY58/5L6dpf+Ov+QCA+wG81x///wC809v/AODPvf0DAP7jWbTbdwH41IC/7YdbJNzvn/nSDY6jKoC/BfCQL/tX4cfHCfe7ERw3+wFMw30kPOzr61EAH4FbuN0CNyZfIWP1D+Emx3kATw1o028B8GH59+ekjt8H4K+9/ScA3ifnPQJgB9wE3JU+cQDA/wDwQQAfA7AGpwz6Hl/uJoBjvk6u9e2bAPiPAI4COO6f5ZD/ba/c7wDwmRPK/m98v3gAwM/4Yx8C0PZt9LMnnH8dgL/39ncAaAAoAagAeNIf/2Hfhvf6Nq3B9e2nABT9OaP6b7n+t/u2vBvAFwBs8+111D/PPQC+Wc4/6W8YMA4GzRv95h1fl/fCjettp+mjvwfgl739rQC+gtP3mQ8C+Hnpo7/h2/NR/wwl3w9m/O/fi1O/Uz4J4IsAvgzXp77if/dAr77g+ultcHPcRwEMD5iHxk6493+A63d/KHX+RV+HN/h//xaAWf/fE77ef8U/28f87z8CCqhe7st6F9x42SF18V/h5uifQ5/+oGOzT9sNA/gLuL57H4DvXse8P+3ta/399+LkPqXtdQ1cv7gPbu6ZGNSO5+pdmJX3XF/wrAoDdOA6+dUnHP9rANd7+0IAD3n71wH8794e95U05BvzCIApuIn9AfSfzC2A95w4uQPY6RtyEkAR7oWti6KPwnnZrgTwuD/+egCffrbrcAN1nvhO+TDcIuPl/vi3APhvcC+DCMCn4RasrwTwUX/OTb5zFuEG54/645P+/7Gv06tlcPyC3PsGAJd5+5vgF1gnlO8P4Be1cAuve05X376N3g036T0J4Dp/fBQuivuPAPglf6wMN5Av8v/+fX+fKbgXam+CGT9N+383gM/7Z94GN+HtgHvJ/pY/52sAbvf2XwD41rNot2Hfbo8C+CMAr5O/7Qfw77z9fb16wpmPo38DLuKuhgvafiaLoi6AF/u6ugvuZWPgXvSf8Od/APmx9e4Bz/urAH5S/v1CX8cH4SbWPf74p3vPKH3sWt9funAv0rv8eW+Fm4jvgJsjXgK3UPmM/+0X4MbHtQBeBjdffLv/zSG4Pj8Nt4DqLdBiADNy/5fDvTyGfJs9COClWld9nrUALn7+iy/fawC8DsDf+ONTcv6v9eoGrl+9y9s/AuC3+1x/AuzXP9Q7B/JS6vOb3N8weBz0nTf6XM8C+HZv/yY4Hgf10ZqvuzfAjctL1tFnsjLD9dHec74dwBdO7H+nGQsfAPAMOLf9HDjGYrgF6TTcQmnIH/+/4LyYg+ah7N4Afgpu4dL796cAfL+3fwBuwfJL/nk/DjdnvQWuHy7BeZQiuAXZ9XBz8q0AtvhrvBccyzcC+KN19Idc3cj5vwHgv+rv/f9PNe/nFkUD+pS2133wcxqAf9+736B2PJf/PZuus37owDXkDwL4aTn+ZgBXSqbrUWPMMNwAfKfw+xW4gQQAn7fWzgGAMebv4TrKnSfcL4HrYCfiFQC+bK2d97//KIAXyN8/YV0K+H3GmG19fv98QsM6CqJHyfyVMeYquLr9FrjBCLgJ/TIAfwXg5caYUQAtuC+ia+FW+j/lz32PMeZH4Ab+DrhJ8z7/t7/z9xqGe0F9VNpV8i9kuB5uwQFr7ReNMVP+3uvB5QCOWGvv8L9f9vf+FgBXy56IMf9sT8G9fH4eLutDE8CfGWM+DTe599Cv/a+He2ElAI4ZY74M93V4E4Cf8fse9gGYMMbsAPAqqa8zhrV21Rjzcrh6fwOAvzPG/KK19sP+lL+R//+ut890HL0WbpEIa+19xpheG64XT1lr7wcAY8yDAG6w1lpjzP1wi6YzwQ44j1UPPwbnYfm4MeY9AP7MP9+pEMMtkupw3p3Pwy3yb4TzEN4PehEBNxdd7u23wi2qPg230LkJwG5r7awxpgnXh2CtTYwxbWPMiLV2Ba5f/IO1ds3Xw9/DtVlvXJ0Ea23XGPOEMeaFcHPR78C1RezvCwBXGWN+De7FPQz3QgWAPwXwC3De1f8DzqN0Ii6A6y874F7YT5262gai3zgYNG985YTftsExdRfcCx4Y0Ed9f/9hf52ftdY+sYHy/r3cb++Ac073Tunl/LkDwJ8bY4pw9XCPMeZ1cHPdLb78JbhFyqB56FRlfTOAC40xPwE3T/4x3OJsGsAK3Pxk4BY0t1hP9xtj7vHPtgjgKgCf9/eJ4RwFPeiWgzPtD2+G+9iDf54Fb55q3l83jDFjcB+hX/aH/hJuAd7Detpxw3iuLYpSAO8BcIMx5v+x1v66Px4BeKW1NpdEzfOw322tfeSE49+EkxMSnfhvAGj6l9iZoiX2mSdXeY7CWnubMWYazoVtAPwna+2fnHieMeYpuK+IW+E6/RsAXArgIWPMRXCLiuustQvGmA/DTSw99JL7RAAWewuy8wwD92X9udxBYy4GcNC6vSHwm3ffBOd1+gk4DxJwBu1vrT3kN/S+FW5Cn4Tr46v+pblh+L57I4Ab/ULj++G+JAH0TaB3puPobIoH5OsplX+nOPO5p4F8P/p+8MPpo3CLAcB9Oe+W8y7wxy73930Qzhvws/B7iuBe0LDWpsbkkp71XjyAe1m2/KKu92y9Z7BwLx0YY3bDeQpvMsZ8aL0PZ4z5nP/dndbaH4LrK2+D+1D8Aly7xnDUFPy/32WtvdcY8wE4TxistbcYJ/p4PYDYWvtAn9v9AYDfsdZ+0p/3wfWW8wT0GwcD540T0LH+cx/u47RXl337qMeLAczBefLPprx6vxNxqndKlpjMWvsVY8xr4ejSDxtjfgfAAtzC6X0n/PbF6yjbg3B7ih7z/16Dowlvl3N+Eo4q/rS19mP+2gb5dug9mwHwoLX2VQPutyb2WfeH08z7XXD/8rlIMreedtwwnnMbra21dbiO9n5jzA/6w/8C1yEAuF373vwcgJ/0HQPGmJfKpd5ijJn0G2ffBcf/rxd3AHidMWbCb0T77nX8ZgXOhfq8hTHmCriJdw6ubn/AexJgjNlljNnqT70JbgB8xdv/GsDdfpIbhRtwS/7r8W397uW/lp4yxvwrf31jjHlJn1NvAvB+f87rAcz2vrTWgUcA7DDGXOd/P+Lb83MAfsx/5cEY8wJjzJAv6z/7Y8MAxqy1n4F7gfYr24nlfK8xJjbGbIH7sv+a/9vtAH4GrK+fB7/4NwRjzOXGmMvk0DXIR8t9r/z/Nm+f6Tj6Crx4wHsPrz6bMq8DpxpDD8EtvHs4DEcnAW6x2nuZfBLA9/n+9EoAS9ba3heyhZtbPgj39flzOHkOTOAWrYD7ONgB94V+qsk39ecAzgv1uHUbuj8E187vMsbUfB/7TvRpe2vtt/rf/JA/dBNcn7nNWjsDR+deDkfzAa6ejvg+fOJG7b+Co6H+YkB5x+AWioBbXPZwqvpf7/x2qnljPejbR41TIf8cnMDibYZKy7Odd0/8/aneKRl8eY5Za/873IL8ZXDj/DXGmEv9OUPGmBdg8Dyk9/4iHOV1lf/3raA35v1wXuYfg198ypw1CI8A2GKc9x/GmKIx5kUDzh3UHwbh8+AHBYwxEzj1vL8fzrsK5N+lfdvOWrsEYMEY883+0PfC7Y06L3jOLYoAwLso3wrgl4wx74SjGa41TmK7D+4lDLjNaUUA9xnnnv8PcpmvwVFj9wH4uLX2ROrsVPc/BPc1+TW4xdR+ON72VLgPQGKctP1n13uv5wCqxslA74FzqX6/tTax1v4L3MR6m/dCfAzswDfBvSxus9Yeg6OZbgIAa+29cK7zh/3vT7UYfT+AHzTG3Av3pfQdfc75IBxddx/cxuf1DFr4srThFgV/4O/xebgvlT+Fm2S+box5AG5zbgGuz/XURSMAPu3vezPc/ppT4R/g+sC9cBPcL1hrj/q/3QSgYK19HI5unMRZLorgaIm/NMbs82W8EvkvvAl//KfhFnXAmY+jPwYwbIx5CI7Xv+ssy3w6/C2Af2uMudsYc8kJf/ss3EKzhx8G8Nu+XX8dbv8MAHwGbv/G43ACiR/Xi/j++m1wdXIAfAn10IF7meyDW2wdhfNQnErNuATgR40xd8O9yP5J7vd1OK/O1+A2s/6ptXYgdSb4KpznqEc73QfgfvGu/L/+nFvgxpriI3C0yt+gPz4IR1vfBbdpt4dPAfhOPx988wm/OdXfMpxm3lgPTuqjfoHyZ3D7TQ7Dba/4U2NMBafuM+vBl+Doul5oj1O9UxSvB3Cvb/P3Avg9v3j9AIC/8WPvNjjxyKB5KLs33EL89wHsNM4TfyHcnjUDtyh4F9yc9U44OrU3Z/WFv+e7AfyGv+c9GKwe/CD694dB+DW4+eUBf+03nGbe/1UAv2eMuRPuo6OHU/Wp7wfwW74er4Gbf84LviHTfBjnTr7WWvsTZ3GNHo9dAFVD/3Cuyhjw3IIxpgzHzZ8Uh+f5BmPMfrj+v54J7nkDY8w/wC02HzvtyRu/Rwyn1mr6l+wX4F5e/81ae/upf53tGfpFa+2jm1XGdZTh3XCqqe99tsoQEPB8xXNtT9FzCR80LgBgBc6l+4lnuTwBmwhrbQtuw3jAcxe/COeh3LRFEZzK6UueljIAftxa+9n1/NAYU4LbdPtsLoj+AI66eFYCEgYEPN/xDekpCggICAgICAg4Uzwn9xQFBAQEBAQEBJxvhEVRQEBAQEBAQADCoiggICAgICAgAEBYFAUEBAQEBAQEANig+qxQKNhi0WVkSGWjdjpg07aRyLiRLMPOJF6unmsHHIfcR4+nadr3F2cfsDcPO+gfA+4z6PaDrtNsNmattVs2UrZBiKLIxrFLWG8MGydftv7tqpv0tel7pjGDWk2vQVvPH/RT7WP59utfm2ZAI+cFBic31sC+sR5dwjrKniTdc96WADA9PW337t17ri8bcArs378fs7Oz5zyyfalUtNWqDwCc63cyn0lHjaOYhwd0wl4fTBK5hqWdG9NyhUiuHUU6T/A++ttEryNlLBb4yrFpInZX7qvzgD6T/3+s94ScK+WV7/0oVxVSLrmn/nhucWVTxqbOtWeMAe1yOgx8P0p7xtImRq6uvy1KJaYJ260r3SguFTN7aHQssyPpX636Kq/vr9OVvqi2zfXb/s9RiLSdT35/NFpttDqdMxqbG1oUFYtlXHTpFQCAepMdq9HRwUqzXGTBqxU2RlHOyZ5n0MIK/TuFDsq4yEaJ5KRWsyE/kEqMdYXWvzOYnDOt/wjsTQZJOmBykQvGpv9gzb2itQeI+cAD92nE4nOCOI4xPjEFAKiWmXpMy2akU+sCs9vl4OhIZ059DUa5SVo7eCI271OI2R2LMnlYuWer3WEZ4/4TtdZZodC/i3e7vI4Ovl6fKBZ4bX1mbZv8hCwvCukbqUz8rQ7vOT8/c87bEgD27t2LO+9cd5zSgHOAa6/dnEgO1WoFr36lCwRscv2O85kplDJ7qMqUgBUZD1bGb6Pt5uvFNb6cbLue2WmHGSO60qfLFV57uFLL7GKBc26zw3fBUlsWOTKut26Z4r3WFjO73aDdMTw/jhj3sVRw5YlrfOZEnq0g5a0VmE2ioi/LhM/XaS7wuMxVH/7EDZsyNuM4xtTkZO+G2XE7YFGqsIPeLX0sM+jDX/pEdZiLlrGJaZZRFtwlWb9tq7LOmwtsq5kW57eJXbsy+9q3vSOzh1vsX0/cxZiO8bK7ztwS/z6zwr7dTtiHWvL8JmL/mBpmXxwqsYxF31duvK9flptTI9BnAQEBAQEBAQHYoKcoSS2W1txXb1dcaZF8XdfKvHRNPEVRNMBt21uf6apeqTktgOnvvdFcjrF+/cvpac4PN2BV3scLdNLpudV6etIJOU/Leni6Ae5Re0bO0jOHtayTnEs950STOpbKjAt8rm7akfN7F5f7YICHRcqS80L1ZwhyHqFIvhjynhrpNwPaO3++3suc9Du1c19s+kUmd4lzbupBlGRAwKlRKpex++KLAQDdLr+ahaXIeUKjiN6RYpFfzV3xrs7PzQAAmk1+nZfG6AUqV8RLI9coFOmxqdWGeb54l5vCFFQbbbkmz58YqWa2bU1k9srS0cxebfK3UWE8syueShwWr0VBqDSlXpBy3BVlDKYJr11MWPbSAI/yuUZvzsg7cNRrpCfzeZLTbiA5/bYQfZd1dU7LUaNK7yk3Jv0sN3fK+18SeNz3uc/wHPE+jsr7v9K7lzIKA6lOebdLEeOo/3OfTfzF4CkKCAgICAgICEBYFAUEBAQEBAQEANggfWZh0Umce6pc4iWGq/TrCmOG2PTnS1Jdk9meW1EpDqXS9P56ufy26OwcdaupPeD8HAa53kz/NWRvM7YZsFk7V65BlNKAskSbTJ+5ezsoFWpSrT+eWzCqWpBzZBMxHbpKmUndaB0oNaZKGnXR5vi2qJ85UK2Wpz+1fbRsJ9OlStcOUuTkb5T2PTywjAEBp4ExEcplt5FUWKo8TSC0QrdLWwUJKyvLmb20MAcAWF3msajIi8d1oZ2KMqZNS46v8Hyh2NrCea9JuYoF3mvxKK/fbXNT7erKvFyH90XETb1x7N4vpdx8TnSF2k50LpNzUqWkkiavfZ7I7V61KMWuKjydd/Jvm0HvgZOPpwNOVZquMECkEov4SFVjsm8eHaG7LISanSUFOjHEDdCTNVKmJXl/pL7saW5+1fdEfzvK1Z30ORFUtf353ZzyfH0InqKAgICAgICAAIRFUUBAQEBAQEAAgA3SZ5ExqHmqrFLiukrjGkSDvJEDlEf9VVZ6cv9oiEq/pBokLFYVhbjnJLaOFkDplJwPcwAvY/vQcErNqZs2H4+ov7oO/Vme3E7/TYGhO1JdkUmOyhJ1QE6p0J9io8BgAG04gA7LuXHzkkEx+yv88gHt+ivOcn3M9u+gJmtLvUJO1iCX0OcQVVqOElS6L3yDBKwfzXoDD919L4D8/KQiq7bQ1l1R+VjLvtYRKilNHMWQ6DwIieM2ADm1lHxLq1I3lb6e5uZ5GeM52lpiz+SUVnJcC5H2ubbG+5FTczQMBswHNjfR47zA3zPVIIUD1L05JbZOsLbfXCfztcSOGqoN0R6iXRPaM+pKvxHVoPaRhVxZWN6KUK9GKLlUlMEtmRCNtG3RB8+MtL1VISf9VpXRRujVeof3rBZ5z4mqK1dhA3NumKUDAgICAgICAhAWRQEBAQEBAQEBADZKn0UGwz5dhyrLjOlHga0vkFLPraqURJ5SU/eduArFZVcsDwjGpSkbZBe90i8FceFZdTeLC1GDC/bbMW/zmqXMSnJyAHX39qfmlJY6myBU64FBf8fxwBCXuUiHp7/2Ok/15yiVpgoHpTb7q7wGB7lcB497mt8NCps26GrpAC50w3mPAv6XRJomaK720nEoNS8Ug9BnHaHPNOVQ1KcHa0/UeVvn365S5UYoGaFeUGbAyG7Mc1LZvlAQKiW2LG+hS/VX2pb0G2KniSjReumU8hOSPMcgdXD/d9RmB8Y9Edba7N2SZ+/7b6nIvf302XSbim+joWEG15wYYQqPir6HJO9YssgUJ902KbN8Psv+9ZN7BUjbRpJmI61SfdYVCq9aYx+pxSdTf/mdFdL/5BxNB1WVvlgtnZwmaiOkaPAUBQQEBAQEBAQgLIoCAgICAgICAgBskD4zAGKvtsm5p3K5ygYETbL91QI9l1xX1mlFCSRVKdJNG5dIkyWxBA9rszTNBu2W5OTpCJWlZS9FPF6W3EJRWXK7WLp7IRmhey5eawcQLf3FSycE8xukcttkWLpsVfmQo89Mf9e9TbQuT1be5YJZmgFkWq7ONKeOqrZ4ThQpNaUKGrl6PplZ33PsAIqL5emfs0yvbQe4fXP51qQOov+F1WdnSm4GALAWacdRG7kuJeOhFKtdkp9Kv5NLmn7zsqolixxT1ektmV2rXZDZ02PbMrsbMzDfUocX6gjdVimTSqnEnDdHI6FtWmuZ3W4wYOPc8Scye21lCQCQdHUbg1DuMjZ1a0Q0YJ7VOSAdFPHwHCPx7RLlVLf9R4Fu19D8YOUqc8lNTk4CAEaLrG+7zPprLy3xeh3Wd35A9t+mMigPaS6HpG41Efo2bTC3XrLK4J1toflaI86OhQ9U2tXI1hWtI60uVSnnNieE3GcBAQEBAQEBAWeHsCgKCAgICAgICMAG6TPneDzZPRXlzuhPkeTZDKFivBusXJVgUxXSZFFC1/BinYqEuRbdsc0GbWVW8p6/QZSOqoR4VLzAGJOd8xXD8sQF5za0XQZBSwesNwcpstIB9nnJfdajz3J1o8ETNb+SujTlIkorZSoROXeQai1H2cV9bRUSRrHmLpJnyKnFBtB9UrN5EqGf4qJ/P8m33yC1ZX8X/WYrCZ9r0GfvqmKqy3FqxG43OH6KohgtCTUQi8IFsU5f33hEXGwijFTdtoFcf83RGvxLIRclVilh7feevpG6iyTY3/bdOzO7PEGabL5OCuxIm20wu0BF00qdbSm7C2BkEi0WWZbRYbbxqOTHmhonVbfL00MAsHL0Gff/JSqnWg3SQxClWi6PYi4goto00/M0NrM5I6fy4t91zKgCtyq005ZptsuQV/N1Zph3rLPKNlG1rs6LuiXCSnDeRJXPGgA5lxNNCizvA5PI+1fyuVmh0lTpVm85uyhBJSNRk+ncYCWvnx3wLrFmQDufIYKnKCAgICAgICAAYVEUEBAQEBAQEABgw/QZ0BM92EHBpnK72HXtJe483VE/PAoAGCrTlbba5LnHFuiCqzd4vJsq15Un8Gj2p4XyG/BVVSWHJefKgsQR00B8w8PO9TtclmBTHaHScgEH1e6vEBmg29sUWClG3oPcP98YcrETTf9zsuNKqUkwOc2TJq77YomqinKB1GnRSG4m+W03ERViIuqLXCGpGExSqiOUwtE8dVn/GEDzalvmKOScaEP/IXnjvkG/QfJMjY5NuspXGiuZfezYbGY3ZuYy+8CD92T2jkkGoLtkN+mUsT17Mjue2kpbVFA5HcrzmFUzEVCtuik6zU1n/R9K1TciSsuNt96YLZQ51oa3X5zZhXHSZ48fnM/sQ8fZfqsdVfzIuEtFfaa5vSBcmozNhSVOqHFEu1LmdS7cznJuHb0CADBaYLls46nMbtbZlzRI78B3kapjzxN91lOdDRJ/qQK4XOOzb9+6I7OHDF9QzSOONkvXqN7TzmK18cscI5XR8cxWSrouFKSVYJwVoe/Kov6OJQ9Ze5nt0pTgkEmd727d82DXnCqtK9cojI3SlmDMHZ2jebW8ii+nPoz6nL0+fGPO0gEBAQEBAQEBZ4gNe4qIdSRB0FW4eFjKQ1wVDnsvwdwyvy5nlmRTloZYyMU6On0shfxRXVly9RnLp1VuA20+f0OGjsToWVx2nyXpKFe2wxrrSLxGyG3CTfvaOZyHL5jsw0JTUsiGu/yetQF1ox64nhcxd4zXG6/yy2Sswi+WoqV3oBQzLtXkGI/bUt211gAAIABJREFUlBsrk5RfL8eX5PpbeX5k+AW1tsDNiGsSO2O5K/3Mf+Hq8+uXl8aIsXrc9PcaqWcpis7BcHsOYnmeX3rHjx3K7Gf235/Zjz54V2Y/9uDDmd1aZvs0l2cye3qMX7VbpiYy+4JLLsnsK19xfWZfdtUrM7s8NMXC1Z6/dR5HBrWyK7/OW/lkQjr/8biRja6xbpgtuzl3fHp3dmypPJ3ZX32EbbAwQ++QOj937aLn7qIL6FlamqcHcP+Bw5ldb0mqCQ0tJvN4InPPqjAB+w4w5s7hcef1vXyUc8N0jV6uYolzydri05mtY1Czpud0Nmn/mGfnGllii7xKJbOiEp9tSrxDoyW+NxuHOcYS7yEy6kpU7/skvamjF9DL2mNmAKDToTfdNLhJO5XGKtR4flnSdmh9VqYkrtUq5+n6cc67zXn2r8QLK2yTnqSOzK/xMD1lBd2APTAViul7/EwRPEUBAQEBAQEBAQiLooCAgICAgIAAAGdBn/WoAzsghktuo67QKENDI33tpWXngp8V+qzR1g2u6vbsT5MNTo/RPzqQkVgoo+OMwzE2LJt8JWPz0jLdfMsSE6ntqbTlFYmTMUp331BBNonJZt88E6WbAQdQVJsGdxON1WF026ZuctPI85rFWLPa+8t05JnKEotiYpTUyJhslI+brNOCZP82CxLXxkgYeKmbYcvjlVwqELqDI0M3cUU2HZqiupI9nZNo5mhN9cJLI0cxDhIW9N9s/nzHgcf2Z/bDDz5Ae9+9mf3kQ9w4vfD0k5kdNzRFjmzWT9g+R+a5UXP2mWOZ/dij3Fj76GO0r3vlY5n9kquuzuzt2y9yxoWk3fA8SbdiwDQeORZ/wJygIhIVPhSkr5enHG22WCbV8cATxzN7Zo5tEMecB3dMcX58+V7+ds8eUm+dvaStpyQG0UNPklabXeG4ylFpuXhe0ie6fKaFBUe3PCwxa3aOc+xeKPN2XOU9jaQQGRS/SFMWbSayOSA3j4hoZ4JU8ZYx0kfJMdKR3RWNQ+TnbqGXKttJjY7uviiziyN83+r7plSWmHsV0ndtefeZATGLdItEVOZvi0XZSlLhfQsV9qM1H3eqK3GVrMQqs7LdoFjj76y+mmjm6NAeZdovnuLp8PyYHQICAgICAgICNhlhURQQEBAQEBAQgA3SZ2maouFDd0vCYhRyofmFWqnIDnVxg3WapEhml5zbrNERpUIuPUj/eANmIGWmUFpIFF9S9labLv3qEFUM09N0YQ7Jc+A4XbLLXhrXavOCS8ui/hgW93UswY60AHlJCXEW4crXBys37J+ORd2lqmQxGh5elVjeHKvwucfH2O5lw7putYXSqpBiG52kqmV8ggqKanVUzqeL9vgCVSrLrQMsvHTQTlviqHSo0BiKRa3mXeqrTcbcqGuI+QHpP3J1J6qa+BwpIs4HBpHSvbY98jT7/F1fvSOzH773xsx+7BEqzpYkBtGQZHAfnmDqhqLEyylWeE6nxX5RX6MKaq1Je9/D+zJ7Yfkgz19mZvWXvuStAIDtNSp5ylPsZybuP688F2CMydQ9Spnl0s4MsFNR+Q5JfXfLbm7b9zTpyaNzojKSlBxjw6ynC3dsz+ydO0mZSfiaXE6kq67aK2XhOG09SuXU4pJS1LyMKu1yqSn8ForZFdmCELNPTo5wvhkeYRmVZ0+7/G1eYSty4U2Fp0PlecsyT26dZrkLolpeW+L8ppnpe0qz4hT798gFpMxKouDKh7fSNB8yp0ecmyOhF9MB85juGlA1oe4zKFQ591e3cV7vpQJZS57JjnU1JYio0gqS6ieqkqazUoA0l5pq42rC4CkKCAgICAgICEBYFAUEBAQEBAQEANgofZYkWFn2IbrFr1uqabZbuu1Gy9xRby1d5AvL3HXe8J7UJFUVz3pc2+vIYJ5TyIn6QDN3N+hWPXaUbv/CNgaCm5gkraZ0UXrcUzR1uuy6QtusqqqqSldylJNySbbhfEpsbDZYPf1To6TSJhrkMtb09RLWv5eBe8sofevDorBoSTC34jDdxVsuuCKzt+++LLOHJHhjVVyxJVFcDItqYXGW7tXGMsu7JIHR2l0JqLlGN34Nvh3EddwSF632Tzsg43Z/Uu35RZ91JODdglddPvQg6aoH7/l6Zj+xjyqzjgRt2y60zcQW0i+1Yc4HJVGgQlzkmiF7ZYFU5soKr7+wRFXaoeO0v3zb7ZkdlxytEE1fmB3bMXIpbymU7eZT1WeOyFPXudQG+i2rqQ00CGCBfT0psI6fPurqb35O0hDJnFwqsN73TJPWuexi1l+9ynFvOy2xOY6W17hNYPfFezN7XoRTS4v7+dscLT1opLg5pCB9c3mJz/FEhb972W7SNGXLcrXXOLfHg1I+bSJ66jdVcw1P8L2yRZS5rUOkldKmUIZ6PR8AubaDirPyyLCcIM+oc7qkXtFAkian8lYak8gppW1/Wk1/oa906Py9ZRsAoNtiGybHJM2IKJBTodMj2TaRK7sGdTyL1+ZzbxYICAgICAgICHgWEBZFAQEBAQEBAQE4m9xnveCNshO+m9All4gSbUjcec1VujJXW/Jb77bL5S9TL53tT4fllGXCO4n4DdUK3fLDNdrVQv88aKnmt5K8WEoJbtlGasB66uhIQt/wSovXaIuiris750uSGdrksq8PUKVtMnIB4gZSeFpn4pqV+u4pBcpCr9mG0KxlqsnGd5Amm9zzwsyuTpG2tEW6TtdAKqVbJK1WVFVcXRQXLd6rOM5zap1jcg4Do9W8IrAdsZ1WJLBbN9dO/dUW2ocHMgHPcbSFvjo244L7PfTgLdmxAw+TMmuJMmbHFgb223XB3syujnG8RKK2MZIJuy39rChVWxH15vAar1NdpBLx+GGO6yOH2J53fPUmAMCQBB+cuoC/K1ZIOwAlPJdgjEHBjyFVzWpgVTtAARlVODbmO6zjnnKrm2qwVRmbQkGNi/L2yBwDPB55nGPnumsuz2zNTH//w/tZlpj9o6niW83nJgFSobY8a48qyale5dTjM6Rhjkl+tN011kXcYln05dcdFBHzXMO3l6p4t0xwrqvJu2d1jco6fc8aUXKWxt14K4+TkoZua8jJFmlHuW0nMqfl4s7mXsD8rb4c4v7npFbnyf7bMkzVUdflKc7R3TW+Q9uLbCu0Jflph3WhWzsSKXvR33MjLFrwFAUEBAQEBAQEICyKAgICAgICAgIAbJA+s6B7LLejXQImFcQ92BVVwlqdPtamBMVLekGtNKiUUmMaaEsDNopqqzZGN/GFU9zRv3Na1EsVcceKO9FAXHKa90sCW+VcjuIr3rrduT87kqenPUM3YEtcn2tNDWoluV1S2XXPu6N7HpatvTbM02dSTtnhHyt9lmo7SJ4zT4m0NHdOwrYZm7wgsyti10EKJFla5m8tA80tNei6r42RqjGpqBpn6OpvrTEAWFXUagXJmRTNH+Hxjmv7McnXMydBOVsS/C0nlByUu05gn0dUWkvc1QeecTnGHnvga9mx+hEGyNwyRZplapJqQlXYNOpsh1jyykm6M5RGOE5tJBRBjfRZXOBxzbdnJE+iWeXY3P/AgwCAByZ57ouueVlmV0fouo9KzzX6DOjtQtBxlwt0qPmehM4uVTn/rYgCc265R59xPGqusYa0xyNPzWR2FM/L+WzLtK2dmvdfXeU4mZl9Rs4hraXsTFHKHmseRblm4u28+km2PSTsbzOL7ANbRiQnl8xlkVJC58k90LullRsWZRtHq07KrCOKs9wGBlHRlsZcO8dFPnsqWz7Stii4dHuL2HpxDZKYY2ZTDYIrgVXrtFtdofikXSKlQ+WivTxotRrnj/KI5KFcYaBWLbuVuSlJSYuretScxWQbPEUBAQEBAQEBAQiLooCAgICAgIAAAGehPuvRBZEGDxOPVSTb2DsdutIaLdrd9GSXcC7o0qDgjXKj6jDdwBdJTp7dU6Q/ynKd5TW6+1ZybjgJKCjSl4qoYypFui01DVqPR2knojiTYIZ6bVURJOLu04Bs+aBZG8/hsj6Y7H5pOiAB2wA3airPG4siYsjnt+su0eVeqdEtOjJBmqQlQcRWl+iuL4vLNWlJ7quE7uDWLGkvzYPW6fD8dpvUWztidx8pqQKKVEOn4Wi7YVGslIq8XkFc2h11C0vVDRBtPOdhpZ/W16jk2ffgwwCAA48xp9iFMd3sOyYZmLHeZJ0cPMbzlUIvs+pRGGab7LnsGv6hwjbpCkVejFnRlTLLMFKTwKqju2jPHQUAPPng09mxmUdI/U1sfxHLVZKgdzk8e/nRCj0aYkCgUOV9uqo+izgeVtbYZ7uej7ei4FLqSCmouQX2AZ3nh6syB8gWiGqZc0BFypu2JNdhQuqtKM8xPc75ese0KE9lzD4z6+aHWVEl5fJ2yTtnVdSuTTleEkpXaRhzvsI39qh1qfRE5pFmQ9tKKC7dulFi28aegkqabKvW8pL8TFSLUX/Voo57K3RbVCrK+bxkq9mU49IvRjgGVf0dpZrrUuZvH/izbTSvGceg3j8RXtd2eY1E3rMdzTmZ9rbjnDmCpyggICAgICAgAGFRFBAQEBAQEBAAYIP0mQEQZWoj8ZOJj82I3ZJgSx3NT5LzbZl+B+XPdJPFEhhyXPPGSI6sWFxsB47OZvahWboZ10RVlIpiqDxEN/CeHXTlDklemtUVuvMOHXc00fEF0iwtdX2qq1LpM6FcSurDlrqL0s113bu29PRZv/YAkChlJm7xolBm1Yiuzq1lRzMeEbdpPESVwPgkA/A1pf8sr1Fxlohru77CNtsuAQGfOPh4ZjfKdDtHFdojI7x+tcbyxm26oKsVBj5L2k41GElwu7I8m9IIqrxURUw0ILjocx1W2rm5SBf8kUcfBQCkLY6p0ghzhpWqpEYPz1L5d+gQ6U20OTZKBbbnjstIdTXbVN60ElLVVVGfRbHkEaxIMM5xUudLxzg2Uzh3fFPUUMcOsFwXitqnPKH08bP/vWgAGE8f2QEKHuVtU6Eh2jK1N0QFCk+b6dNpD7Wpbnvgce3TXdle0GywPUalnapyjqqPcqokKUS5xOO7dnPOHR5iP9t9oWvLBx5+JDt2+Jiqn4SSki0bidB3UU5mpgExzw+ybSeFAXOKvDdy84tuUxEVbS8Xpe1wTBUlWHFBqLacgqwjSrQOKc220OZGeO6ownvWhZ5rtISCjEShqO85oc470qnavi+MTVFFPBFz3o2L/emzRN4rXaXSUqFGQ+6zgICAgICAgICzQ1gUBQQEBAQEBATgbHKf9dyAxf7rKiPuvs6g3FHi47J9AnOZXF4zXrtWpStvapy0TBzx2ocP05V38AgDKS6L+k3EU6hITrSd2+m+3T5G921jiRTAgSOk5I4uOZdjXdy0ucCTvE1up38k1a/B//KEyyY7dg1QiE9Wn+VctxKUsyN2UVzhsbjoV555xl+D52punmnJj9Vt85yoSTfuSpd2JArA7eOkSGeeYf0NSW6t5Q7bvqMsZodtXCrwmju2UT018/QB/2x8/pqoajTQWqPd372d4wWUDs4nlHsW0Z/SS8V1n0hQtrju+nckrmp1/xelfcaEzq555RcANFc5Xspy+5kjdMWPXyjB6iIeX10TNY30uaLQmkOR5CXcQrf/scdd/2rVee3WEq+XSCA6QNU+OjXKFoHzCAvA9oLkynHNK2VyqjSe1ZZAeu1uH5o3FwFSLp6jh/W4BNqVOWytIbkhJQ9WRRq5ViElEwstXZUghO0uacwDM1QKvnTLxZm9c5sb+5MjV2TH7hFV4eMH5zK7JOW1Qr2YgiqqNPfa5o9NY0xGh8ai3tT8XU1RZ3Vz7wpC3y29/tte5vhqzHP+S6UfWAmAmCZ6XAIXy9xVmd6W2YUxUuRrC6zn2VneV9/zGJDvLNWAmT5YaqnCfjA+ShWiquUwYNtCW56pK++ms1nZBE9RQEBAQEBAQADCoiggICAgICAgAMBZOJkyF5buFlc6TLyR6h7MUWZ9vPi5fGQ55ojrt0KBxa6UaCfClSyu0q3bFBWFeviKkgdt6za6/SdrdOetzJEyO3yUbsMjy6J6SHq5wwZRXf2psVycylxlCI01II/WuYIxBrFvw0jkcJrHTt21dgDFFpVZZ1NbHFXWmiF90l5mPXbrdKcXyqQnNVdWO9WgYzy+0pK8O9InVhZIt3VU1aJKDOmrbclhduwoFVOpb5VU6V+hlZRiHKwxEwo40jHxbNJnSqH074WqLEzk+KqnH9rizk9FAdqW682vkJqqiyKmOkRKKxUFStGw/dGUMZuyfeqqJJK5xIhSaqTE/jUVaWBCV7ZUaJs1pQnlPsgFSn12KDOFgWwhkAlVcxFqS8YyZrWfFuOTv311ujG5ibb/9gW9k87nLVHwQvr6hGxreMmVkuuwQppzfII0ugabjMu8zuSEKAzL7jmGCqRyTJvXXlikerVbF5pfaTKpl1jnL7vZQXLh+VB3/6Qj2w1Wub2jKO8weW2hrA2myi4/HyZ1XkOD5up7WCl+bXNV5OXFznqObD2Qub4iquKyzNMFoQc1R2GktKZ/z41UeY2SzNGtQZSZqNmSBuf9msz1iDfOnwVPUUBAQEBAQEAAwqIoICAgICAgIADABukzCzpTI3FNxhrwT3aFi+ctp7Lqaw+kiyTPlrjuY6UnxK3Y7agrWQNGys59ccvPHWOOrAWx28K9tWQnf2JVYdRTdCg90p8mjPKcGU3NlzXAbbgpsFS45EVT/anAvEtd8thZtvf8ilOSFCK6We0a3ZxJg1RHXGZbdkG6pWvpDi5JsLLF5qHMrm6VHEkxg/c1u3Sjp+1jmd2W4JCmSbUL2qR84ti1sTVK2fQPqJajPHNBSfv3g/OH0/WZ/nSuNnkkyYuqo47y6CilKL9ry/NWR6kOnNqyM7Njce+bNvtCVxRLh59kMM4lUTOupaLUseKuL5OK2TFBdWO1zGmtF9zNSv60yih/F2twu9w34rOX76wf8iVTCkLOkQaMLVtIUsQh6vXrSKf+/qEcc3OVnKPBXFtC90RCWVxy8V65Dq8ZF1nfSmm2OpKrLCE9onNu1ecrrEvurTkJ7NoU9a9Ki02sY1mUaKJWLgvds2kwpK2szClteVeVJb9mWeY9IwEW7f9s78vDLKuqe3/rTnWrbg3d1dU0M42gJESwRWQwDeIQDcZZfCbRiIkm0ThHn4/34mfap3FCYvKCGKPyOUbUFzGIE2JEAYdGELqbRpChGbqh6aara77zfn/sfe/67Vvn1NR1u0vf+n1ffbVr1z5n77OHdfZZv73WYmec4d1WHFTrW5bduaLS0/k+XTvCz0vrd4YtPXNKeRfo2n7RtdYk6qt/tVrxDvZrvVka/2ZFx2v0MR/LTnL0nNQvbA3LlH+G1maOrAl76FhNJrP09WuaIoPBYDAYDAbYpshgMBgMBoMBwAFYn7WsdHLRCXWKj1MlyxNSc2c59k3MGflfbBXBaVKzs2aM6btI/59m6MOH+MlyYpLjoKVeSlYfEb3kr4gcqUXqd4r9k2GrKrJwIbO4yMKr2V2rCAfXVoe72CSFSklUvoUmOKadpqfCs+SgquAZsjjbu/uhdno4ryrSgX6ipiiuGvLk/DOn6l1X1zKFvKqPe3tITUz3z5CjOVdWirRZ3U/3Cc7+KA5XjVS6bL3IseKiMeMJKofiuyOJPkseTwav395epTkOC842+3p1PCcnlQKdpFhIWVJnl/qUSsvQmORAcYxEqQBHVlJZp3UNZlVdPzPDsZwoXdd6944rTdqK8dR/GDmII9o1T9aPscXZyqDP2pZBKb4W2RmscHxI6td+cnbbOnnADh15jkommtSaJHnXpL4pV5k+0/wBio03OqHUz213PthO73pIHf+VySKxOKTvi5Mfd6w+0+H+nneSFfCt9yidvofi260bUieAmT46VlHWZ123Th1DDvXrXAWuQdcQ1hhT73WiIIXmI8ctq8+o/GTLtXqIR1ik583Te6VIFn6Ffu3XSI6TNZcjrrVJ60GIyitmtK/yJe3PQq+2IZcnKote5Bla48NBVggJ1dreXdQunRO8F+gpaFt6epXiYytL15zvCEE6TFNkMBgMBoPBANsUGQwGg8FgMAA4EOeNrQSp3FnhXCMVX1+THY9pmchRY4s+40qYhSA6J0vq3oiwYiuzqK1M+UT2GlRVskO7WAmXEs8ssTVUCxUuRLHiiLJLib0TO1brAhzQCFZ7i425xv3KFkgta4PhfqKxyqr+fXiXWhm5rNJUI0epqrdvnVImtSyNHzEczR5V15Zyqnbu4Scps0NIzR4ny4aJaVUft6wgp6aVHqo0KB5RNJeoLSmWaIfeBWALyY74eJTzNFFL5Gxx7RpPN60aULV5Y3RPOz1NsZCKZH22ql+d7OXIesVlqL+z2rc5csQaLRqiz6rTSinMjJGDx33anglqTy5Y0Y2s07m15gi1kikUtV0rzvosk0UmODuMThpEjhQ1zU4KUdD8oRKNa8GXqXNMQKYtqd/ZgWszWTyiXCFqimLHsYPM7XerE9fNt2mssmqFLBzJYeOJw2TdVNHxeWCnp7m33X5PO++R3SpXRJT6GRlWebBuFVE/7ihte1nn0tg+tqfsIkJHRvEkK0SHkTPEHMUEk0mSJPRurY37uV5crda3A0fqXAdZarNz1uhYBlFj+VVk0UsCrkEyMEPleymdZSeQkTjkF7mu8XywPHYzapk6M8GWw7PlMgBkyFotSxZnMcVs9JnBYDAYDAbDAcE2RQaDwWAwGAw4EPqspQZkB4xkfcAn2tkSLUcOobKswm3/To0OpinWJbPBGTn6inweRjFfWMWXEoct4sYWQ7cl34Tb20MOzvLkAs+B2870RnfV+A4upn5a9UbUJT23JPdZjWOlVTwldvgajSeXL1DsqQmNNfbIg0pTZch54+pjjtFr+/U+QjTZMRSvroccNtbH1LqtVr1X0xOqxq+Mar2NmtJwlTApx2eU1quSI1KmObnfYosgSq+Yzw6eU83kfFJ/91H8v8edcAIA4J7jTmjn7SInb1PTmu4hS6eBXlXpF8m5YrPAk4ssb6Lmcj8rzVFsksgiVfvEjNJnrqntGRjy8+Wo47Xtw0epRVOuqPJoRVBmhHxPHw478TQAsWM6Jm4bHCcxclJLcm6vWn/t2e/n/RTJ5Iaj+JHk9DAtRh6v+zI5wOXYeFPTamF4zwMai6tc4ThkfPRC5cPkPl2b9+tt8Og+b622d0wzs04ps8EBvcfaYaZ/j2inhwra3nvvVxrurh134eCgRZ9pO8okayandd3lyfos06PrqkGOcOsT3oq2PKqWfIUhpa057hg7lY0YLRZSQhZn5PCUktERlIWsGGbCmVZrBqu72n6dHxWK2waSu1EsM7Js4xiVmXkPsiwMK0ZkGwwGg8FgMBxK2KbIYDAYDAaDAUuNfeZUhcony9kZoWvqCfmZsqpDiwPkeKmue7J6UKXWIqeIyY7JMtnoaHs72YzSSEzHh9LZgRlnp9FXvIckh4ztu5H6jpygsVOzPqIOsnSiv8mUC9XZbKa5klwuSIqFGzvLZMu/ZGvDDNMdQTW/b1wpjaESxfQhh1+VSVWX7rxL1cgz+3XODA4f3U43SkrD5Fer1cL0o0qZNfbdp2XqStWVSe0+M0qOzIpqkTIerOH2T6hzx3qDHG6mWEQyOJ+djrmDQs84KL0y28EoADjX6CjfKq3pIjlq3HDmWQCAPXu0vx8eVSrysT1KUbr9avnVJPEyRFUOjaijzVyWKWmOUaXlKzUdt/G9Spnt2aVjPj32cDtdKGhlhx3tx/aJp53Zzhs8khwCFvQ5VxxyebgR334em2pZ6cEayarerNIt00SnSEPplrWrfZk9Uyp7RieZuicZjuS1zmKzzvEg+fhCndYMU56OzUf1RtNlpUoe2K0yodkkJ6ut3xmyJiPrqnUlpdL6erT+KY7DOaPPXaMYYtNjWk/XwO9NdrpJR02myClqcZUeDyj1qbzKVMjZYrBcm35UnR5myKFh3+FKHQrRTrEsmt/yml/LziVbTUd7gag8W5nTGp/xa3l6j8qSKlGD0fzL69g6opKbJMsqRAPmskuXtaYpMhgMBoPBYIBtigwGg8FgMBgALJE+E7A6NyWGDunPKmTpkKc4UkU6XV8N1kuNOluHkWqO6Rx2Nkb1NBvJlhixIpDUumCkqdv42kXEIaPb9RZVxZvPkJMwVoNy7KyoT5fuhGohEKH4SoTU/mNVKLWZY9C1LB7GJnTcp8kR15GDSln0FdWazJEaf2yHqlTH71cabpychY2c9Lvt9N57lMLpp1hm/VlVx0pD718kc4oxUZX1Q8GqqlKlcUqxZMxEzsqSLTIi+rPbjji11tlZLm09JKvLyTAI+T4/RiefdXY7b+eUUhybv3dlO/0YUVrlSY1LNTqqlM/IPrVKG6R4TOUppU+zFN9olGKZ7d2j9dYrSucVe7T1wyN6/3P/4HkAgDPOPb+dVygpfZf+XTh/f3XbWm16Zga3bbsVAFDKa10zU0qNVcgj6eqSPrdUtb9HxyheYM479hsZUNnLFmejU2RxJPx6SKZim3Vd16AYXqWs0h3FKD5lcuzHNEvgZoKlZB9RZquKRJnltexjj+5opx/dRfOExnJ6UmnZcrm7MSYBtF6cPslyhLq2VlGZOVFWOVkgSizXp2k3EZ5tUum/qQfUqq5JMq90uB4TyLKj1JSQoWzBGL+SUhzBRkbhKUdcplTWTu30jjxnHlP5zu0VosmyffqeaJDcnSYKtNGk/Ud4viTL6vlgmiKDwWAwGAwGLNVPkag7gyxrXmiLlcnqbr5KWoLpcf3SK5XUpXhPzn+50Pk8VPlzlTVC5Fej2dQvhXqNDyjrpXzQq7kAjVCKG6T4wFjkr8eFPK20h7RDq3pJs9Ugl+50SCwKP8JfUwdBu9DSeLBWI+oz0ho1M9yXiujQdfjCrFa0xMyMfiECuEBNAAAfZElEQVTk6eDsMEVS7+vXL918hnx0iPZTvqFz6ZEH9XBto6aahVUDR7bTNehX4tTMfkprG8YqGrm7Er5UGpGyhcaa+qKREomZ+4s7JnNQNEUOoHAL2g4+/D2/5oNDfkiInD0ypAc/z9v49HZ6kOb95uu+307vuufXeo+9+jW4e7cefs+T/5GcsOEFRc52qrXLU5icgX6dI8es16/gc5/1jHb67Gc919+7l0If0EHdGIszanBL+ApdDKrlGey4a1urtnZ+vabaBEcTdSc0hIbUtUyVwyzlvZapVNIDuGsG1rXTTf4Kn9Z5xAeweZ7UKlpmfJS/8snfTZO1IppusMhjeUo1sZFKMcSIWkuRWVb36fquVHR9792tsiEnFG2dlR+O31HdNmgJ9c9KxO8b1hpVKTTSFB00HiiRH6KgnXMzWrZJRiLTD+izN+h+fet0zAsUjon9FDUqqmWfnlZtraNOLDqV3wUK1ZLlMCZ0iH3yYZ2jM3u8HGhSmBOQ9lAoBI/r0bVebuo7gEMwNchnVbnu8+tLMFQyTZHBYDAYDAYDbFNkMBgMBoPBAGCpB60zGRRDGIACqfVyRJllKMIvR8/lcBbSVPVcKahbHdFh7Fq8Tqrq8TFVA+6oqlqefRzsL5M/ihQqhBW1sfuiZLW4JDMNbdosT4chh4uaLjhtb50OJkY0CzMuKark7mH2ofmIGhCmzJLT2ajNPj8jyQfl99MBx6kZLVPIa98UyOdKIce+sLjVeh/2J/XIuI59pa6Hd6drTF3SYVG9pYavoednV/nRuEe+sJIpqUyKmryrSKT1aGzTQp7TNxI/jgv9VipqTx22WkXHU848qZ3u6dO+v/1mpccf/bX6jqruo7AP0zo+GeqsAvlUGSbabu3hSrEef+Lx7fQpTz61nT55w5Pa6fyqoILvI2EidDgYTAstAKk+z7qBJhDkRZZkazavB20bGQpBQ7Q0RxJnWrIQxKtkVFb2ZpSCOmq19vV+kmdjU0RnUldOl7Uvt2x/oJ0u0vjtmdJ1F4WXYMMVWkxCFCnL0aGsL58nOTExodR3s6rPUSX5jwIJDVrsTTqrwf6WugURQS68L1leNTlEEh9bIP9Fk5NKX0lJwxINDK4O91NEVNq0HluYeejudrqyXyns4mpdUwUyfKhWdY5MTGnfIkv0c0nb2KBurk/o4f7KqPouq5MfJgSKKzLQ6qGwO3S4epoMtMp1nU/8Dpqpzj7eYgetDQaDwWAwGJYI2xQZDAaDwWAwYIn0WTaTwWC/PwHvIn8LdPqc1L35XLK/C8dqbPGqur6cnjgX8pMxRerNqXFVwU2OqVqPff0003yLuOTT6Jk0Ki0Cl6GQCEHNPNyrz9xLau0GWYKwqjTt3gcbLVVjFJ4ixU37QtKJdFyK1Vad1KIVCkeQE6UCOBQEh5/o69NG1sm/R7mq17K/oWoU1Z6flUNNtPJSLA3T+JPkZEefHgT+zDm4tq+P5PrYApOjTE9S6Id771Mq5PbtN/u7NWmt1SkSvWrzUamSlWGfjlX/yGHtdJksimSa/OXUdS4USI0+TKEK1h59eDs9RH5Xajmt656dSg3kAlW39kilCEbWaZiPXJ4an0KlpYUv6DYEgnwmF9rA0eVp/VRJntD8rrMBIsniFm1Treg41Rs6rpmCyt/eAR2zbE6tjGo1bUuZfK3telj9JzFDS65nkKF1xa5yeij80cAqpWcKNaVaa6Pe79VYRedeuaz0q9SJMqPv/YkUf3dZtkJsJFhsdgESxiLjkuUhv54yxPVVZ1Q2jtYoPNSgpzsHh9T3lmTUCs9NU9gMuq5OkeknyTosk9PjK2ncP8uPSaiVH/jdxqFV2AKMLZyD3JWirvUMCZMqHb+YKetz1FJ9XWn9uezSDOsB0xQZDAaDwWAwALBNkcFgMBgMBgOApVqfiSAbVGuxA79k3bJLoCc8KCJzoJgyTtVuPRl1UpUjZ4jTFVWTUXDlBblekwS38T4/Oc0qRI6G0d+j7RkMFhJ5kJVZlSmzFMeM3JLIGSS1t8vqehFpW/0wjcRO4ThKfINUpBlyyhY5fmzfL7nO2DEiO4kkSxpSi9aZhiNeoFSkEBEzqg6ukIPMRhTeOdm6KrJ8CUUiq4UU9XtMhyVTq46+Ow4KfQa0veK5qDoaK5rTE0R/7Ni9u52++Vdb2+l7dgSrlbKq5QtO1dkDfRTBnRgMDh+RH1S1uMtReJgaue+niZEl66nGgFI6YzTPqnuVJntwTNvGlGA259s2tFpV9Oef/+J2et0RZCWb4/kR8cdIglbTHeq7Vq1h90OenmD5wPIkcijLjlWbTJlRCJ5ASUQksOO5ofk5oqYKPUo/ru5VuTy8RtNlGu/4lABTU1pznq3PakTNjuncGhvXMa6E+ec4tEhEW9PapH9Qt0TSn8P0SGbpdMti0BI1kYPKTLJjzDTnoDVywvhYkIe1QaU3VxGVlu/R+d0ky68mL1SW3RQ2I7K0TaPi+VXJfR5ZatMzET3XCt2RY4s3tjKjdyhTZjUyf2SHydkE8/Cl+Fc1TZHBYDAYDAYDbFNkMBgMBoPBAGCpsc9Aaqk0CiFFbRWHhWLLtWz4PzmyIsuDQkGtVHIUB6XYo+q4ap2skUiVy6f7my5ZDciOBompixwyZrJapi9PjgODk6066Yyj6OipDvIaiWUkUpV3PyZPtmXdxRYRvF2ODFx4jDlWGo1bc27VZRR9mfflFHeHaacmpasUMEl6yHqExqmRIbqPKdqoFcnWZ9lgGdVsMAWWRnslz/1UOuVgGBg6pUN46rDTS6YUZ8hKZIIiq0/NKG3RDLG2shQrKiuqlq/MKJ3B1jM9ZF6UJwecjf4clddrhceKKXeOiUZjOzaj9E6DqIFaNHa+/L33KSXzlA0b2+nhYY2Tl2WndKljzujy2hSJnDC2kI18ibKc0/w0p6Et2eJSaCcy9ESNHAY2yHmf4waQlU8zQ9RIZPHGFkeUJmsosPNEMldjGruVZqvkVIe60XuG5kMUaEyTsYV0F9F6nmh6sWVhMyk7pq/YyjBY2o7v1z6rkNPDoX6lrftHiCou6/urwc4eKU6psFPJ6CGSO5rXrKN5IfS+zlDbir0+P0f3qEzqOi3T/GBqnTcRUXhUamUjHMNZSnxC0xQZDAaDwWAwwDZFBoPBYDAYDACWSJ85qBoysn5IUdlGqtrIaRVZoQRLAKaOWKPJcWAajmL8kJquROpvoZhspE1HI0WdliEThd5Iw0oxf8jqoU5WUG3KgFW97KyQ6onUyin18E612+SZCPU95XM7G0xHRM9IzjJJj1kPT8xdHdGmbPVBlFmWxjJL+v/I8oZVtLlkFS2IziFjhojaiZwqZpg+8+1puGT6M7L8SbVE4zp5HnSfCnXQ+d5ssJWG9jPTS0169lxB10yB6NB8iE82QDGXMiw6iKrOkGPGLN2Px61SJ4drxKBkJZk+y5BlkPC8oDTT4hwnsRIsVarTalVTo7QjugBFjsWlyThgF0/qVvlu8aLSppQlskpKdlLLMSabkaNSumPCWk9rfU+KBSbHDHMNdphI/ZrVse/pVcu/EjnnmyKngZUKObsluRLJSJmdF1kJIu1dxNZ3vGZn01DdR/vcCWXxMQR6J7rkMkmeYptEP85MKO05Q7EFi+QQta9ENNYqslYjGZXl93PkYFLrakTUJNPctMbpnnXq81rLuozk1BQ7mGQqMfaIm5jNsVfrB3DsxDRFBoPBYDAYDLBNkcFgMBgMBgOApVqfOdd27tfkmDHZZAohOl1PjpeY1nLByVqG1IQRbRPVn6y+rZJDOUYUuypyAkWqX1L3RSfnudpMpLedlYwss7jFUf167xzRP47Uis1uaeMTIWg9JVNmuayOU50sRlgtmWbBorQa0xvJKvEsmbswXcr53JUz9Mc0WRuWG2ThkNU5mc0xlZYcJyopzlts1ZP8zLFvxGSna5H1Q4rl4/LCwQXLC7bkzJDFX4MIXWItMD6hbd33iFqkDARLs/68DtDEtKroy1Oa7svrOPSUNF0jy7LKjK67yXG1aqKoSzFjRRZYuXwv5es9M+R4MVMg6jeo+lnln+Wx57GKzPWoMfxHiuPRbiGpBrYkjNcgU/DJ8qRljcfuAvm6tMeOspnCjmQe0S0c44wsh0tkfeQqSlk1KzonYuZytkPATLRgKZnCtvDa5Jhj+QyvcRwUtN5F8ZgkI6Lq+ahCJLtmD1I0JhQPbpqOfMxM67rj4wM5cq5YoCMJbJ0nTX4f0HuC9gJpVsp8lCSfnVsn41Joa+HYiXRDdvJ7IMS2aYoMBoPBYDAYYJsig8FgMBgMBgBLtT5zDuWypyVYLZ3JJiursmSJENn0sLVEUL01SN3XTNmzZdIcRkaxb5Itg7IZcoaWom7NEZXWYKeEjWRnVu37s7MtNlJhtXyWVdUusXzsLPBgUC6zHbpJ5GCRqSG+ji2EiHoL41olejLNCaUIO7xMtgxharNB+WVSnZZ6NPZPkSjKWkad+k1SzKBKjem82TQtWzOxGjvNqoWRSiJnDs43iATlMTskzdBk535mirreKFAZpTkQ+rBG83WMYiSVy0pbF0j9zg7XKkRdZoXKRLHSyLqT1l3dkUWK0zI1mlM9vTpHRg5b1U67hHkXWTAimaZtxqaK2nZ2XNhliADZ4KwyWo0LMPON5I/MloWZNGqM13SUz2uErIyIMoliieWIPqO3TCGv/dfbq84Eq+RAUCKWm+izQLewlV1EK1G64VLeC9Qv2Ry3Ra3iugmNHcljQgUSjmUAsRUrwyURRJL8HozNgekYC41ho1ZPLO7yVIbfg2kOi1Nin/FqbMmEyPqXrUvjtyzVyS9uPtrAseyWLmtNU2QwGAwGg8EA2xQZDAaDwWAwAABkKbFBRGQPgPuXvzmGeXCcc27tct7QxvKQYdnHErDxPESwsfztgo3nbw8WPZZL2hQZDAaDwWAw/LbB6DODwWAwGAwG2KbIYDAYDAaDAcAybopEZI2I3Bp+HhGRnfR3Yf47HFDd3xOROW0qReQGEdmQkH+aiPzhIut7toh8Y7HtXMB9cyKyvwv3/TsRuV1EtoTxOHO56+io7zoROX2ufBH5toismn116j1fKCIXhfSLReTkOco6EflOSL88zEfX2SYR+Z8icreI3Ckiz6X8Pwx5d7fqDPk7RGRkoW1OaFdeRB4Qkb8VkW0pZT4qIs9c4P3OCeN6q4j0zn/FgUNEJlPyXy8ir57juvVpz7zSEJ5la+jXG9LmWpgPrXK/6EI71onIzSJyX/j9UxF5SUeZT8+1FjrKbhCR54nI/1pie+atS0Q+KyIXJOSvF5E/XUq9C8VKb99yI8i0S+jvd4rIpkPYpDbmkwcrGcu2KXLOPeac2+Cc2wDgXwF8rPW3c+RcJAUismifSeKRcc491zk3Mf8ViTgNwKI2Rb9JEJGzATwfwGnOuVMBPBvAg4e2VYBz7nnOuQVvAJ1zVznnPhT+fDGAtBdVDt7jzO+EDcw2AJ9DxwHHIDz/GMDvwY//ZSKSFR9O/uMAzg91/MlCXzoLwEYA/wng63OU+RcAF83xf8YrAXwwrLGZ+QovZY0tFM65f3XOfb5b9+8mwpgz/t05d0qQZR8B8I9zXP6M0P+zPgIOsE0C4BsA1gN4qnPuKfDz9Whut3Pudc657Qu87QYAzwOQuClqydO0ixdZVyfWA1jypmO+ubuEvujEehxA+w4RKgBeeiAfat3Cb7I86Dp9JiInisit9PdFIvLukL5BRD4WvrLeJCJfFJF/FpGfiMi9nV9FdL/tIvIlALcDOEJEHmppHUTkveEr/3oR+YqIvI0u/2MR2Rz+/7Twdf0eAK8MX3sXdNR1QrjPL8OXGmtYBkTk6+Fen6drnioiPwrlvyMi60L+60XkJhG5TUS+1vqyD3X8XES2AnjvgfV2Io4AsNc5VwEA59xe59yuUPcOEflI+NrdLCInhvy1IvIfob03icjvh/ySiFweyv5SRF4U8ntF5AoRuUNErgQwr9Yi1D0SvtB+Fb7g7hKRL4nXxN0oIr8WkTNC+deIyKUi8jQALwRwcRizEzpu/Uz40Df/BuDtzrk7AOyheteLyH8B+AGAEoB1zrn7ANwN4Izw4+DH4kcAjgLw/tblAL4enn0itGd9mAMPikhFRPaLyPtC+/eFebxFRD4Kv/kaBvCXALIisktE9orIWHjWc5xz9wMYEZGrwjy/MsyPTi3X6wD8NwDvC30mInKxiGwL4/mKUO68MIevArC94x6vF5GL6e/XiMilIf2qMM63isgnhTYOIvIPYR7/jOb3JhF5Z0ifKCLXhjK3dI6R+M3nxWFubRGRvw75R4jIj0Od20TknIR586zQ/1vDXOwRr9n7GpU5T0SuDunniNew3BLWXT/Nvw+LyC0AXs51OOfG6c8SDiDIWZjXnwh9dW9o2+VhrXyWykXthN+8rAUwAOCHIvLDMDc+KCKXiMgWAFeJyGRYP88I/XBL6PcfdPTn7QA+DOAvAJTC839NRK4RkbJ4y6hfAzgmjMteEZkSkVEReUto44/Fy+zbxDMBu8Ic+VRr3gScKyrDWzL1QwDOCW15e0cfLXruhv9Phr64DcDZEmuiXytenhxw+1Yw6ghyrvMfEuRcWF8/EJFjE8psEpEvhHn3axH5y5DfH665JYxHS86XRORbYfy30Th9SLysasm5TnlwXVhrm8OYnBPy+0TkqzKHnDskcM4t+w+ATQDeGdInAriV/ncRgHeH9A0A/g/974sAvgz/8jkVwK8S7n0ivGPM0ynvIQCrAJwF4GYAPQAGAdwL4G1U14dD+oUAvhvSrwPwTynP0QegGNK/A+DnIf1sAKMAjoSPrXhTqLsHwE8AjIRyrwTwbyG9hu77IQBvCOlvA/jTkH4rgP3LPBb9AG4FcBeAywA8nf63A8DfhfSrAVwd0v8OYGNIHwvgjpD+AIBXhfSqcM8SgL8FcHnIPxV+sZ6e0JbrWvmh7hH4L7Q6gFPgN+k3A7g8zIEXAfhGKP8aAJeG9GcBXJDyvO+F/4IaDHUMAXhnSJ8O4JsALgRwKbxAad3/MwAuCD+3ALgNfnP3BgATYax3AHgigGsB/A38Rmo9/Cbs/1L7vwBgDYCddP9VADYD+IfwUwfwCwCXAPgqgIsBXBvK/gzANSH9xDn6s90PAF4G4Pvw83EdgAfgN8TnAZgCcHzC9WsB3E1/fwdem/W7oZ/yIf8yAK8OaQfgBSH9Eeha3gRd8z8H8JKQLsKvo/UAtoW8v6LrekI/HA/gHdD5mAUw0NHeIryW8wnh788DeBu8Z/4HAJRC/icAvAp+fv2Y8v8HgPfQ/HvXHOvmjQDuCfU9PqXMfWGu3Azgr1LKfBbAFdD5PI54rm9Iaee3AHwstHOE7ufgN8PvgF8n14Wxfyi09fhQbjihP18PP+8nQ96/hDqa4XlvDfkfhpdjvQBuBLAfPl7vNgBXQtfCcSH/esRr82vh+U5GmF/w8/DqlD5a9NzlvuiUL9S+4eVo30r9ATCJ2XJuU/jfNwFcGNJ/gSCHOq7fBJVzI2H+HAm/ngZDmRF4OSdhnD5F1w/By7k7oZbsqxLkwXUALgnp50Hl3DsBfHI+OXewf1bCQeuvdPz9DeexBf4rPQn3OOeSOPyN4fqK8197V3f8v0Vb3AwvpOdDD4DPiD8LcQViyuZnzrldzrkG/KZjPfzL5PcAXCteO3YRgGNC+VPDV89WKG0DAGdD++ALC2jTouCcmwTwFPgX0R4AXxGR11CRL9Pvs0P62QAuDc9wFYDB8IX9HAAXhfzr4F9SxwI4F35DizBuWxbZzPucc1udD6d8O4AfOL9StmJh48Q4wjfDjcO/NN/S8f+z4Td9gBf4G1Pu85/OU1KT8JubM0L+dQCeAN+fR8FvLB6EF8YfhO/j7wIYg38BniMiL4XfFO0D0IpZcV+499fh52OdnnU1gF/BP8g2LKw/NwL4snOu4ZzbDa/lemr432bntWERnHN7ANwrImeJyBr4jf+NAJ4FP2duCmP9LACPC5dVoetq1joSf7bvKOfclaGOsnNuGjGeA+DV4d4/hxesj4f/uPhz8eciTnGzKfGT4OfKXeHvzwE41zlXh+/zF4inWf4InqY8C37N3hjquhD+Rd5Cp+zhvvm4c+4E+A3Ku1OKbXTOnQZPtb5RRM5NKfdNms+7O+b6+pR2DvMNROTjQSMCAP8BP95fDH/fDz/ftrTG2Tm3Dx39CaCMGBvh59/9zrmPA1gjIoPwsmk9/Ng8AX5jsi78PhN+Y3WHc+5+51wNfpPB+IZzruk8lbUupU8627GouRvQCH3RiTMA/Mg5t2+Z2rdisUA59wXMI+ecc3sB/BC+7wTAB4I28lp4ObcOfv7+QdD6nOOcG4Ofd2X49+RLAXSu9RaS3r0b4d+ri5FzXcfB2BTVO+opdvx/quPvCqXTwkh1XrNQtO7dwMLivr0D/oV3Cvxk6Um4F99P4AVT6yzVKc6580OZz8Nrh06Bp2O4H7rqLCoIm+ucc38P4E3wO/6kulvpDICz6DmOCpsrAfAyyj/WeXrqQMF92aS/m1h8fD4+W/NPAF4Lr83qxE7Q+YyQ3hl+BqF9cTT8QnfwWrdR+K/6JwPYDT8nJuHPpm2F//J/QXhRvwhey/R8eC3M96i+Cv1uwH8lt541i7hPDhRzrZcr4DUPLwNwZXh5C4DP0Tif5JzbFMrXQhlg4euoEwLgzXT/451z1zjnfgy/wd4J4LOyuIOared4JoBfhA2VAPg+1XOyc+61dM1C5MgV8GfYZsE5tzP8fhReg3JGUjnE87lzrrfkRtRO+I3YaVTXG+E3py58iM2Lzv4E8LSUotwPx4Vyn3D+DOK34Od+Dn5tvQp+E/ZkEXnPPM8LpMvwhWKuMSovtC86sJztWwmYS87Nh853j4NnONYCeIrz5+p2wzMmd0Hl3PtF5D1Bzp0Bryl/PvzHSRIW++49ZDgYm6JHABwpIqtFpAj/Fdct3AjgheLPGQzAq+rmwwQ8b5+EIQAPh5fAhZh/AW0HcJToOZiCiLQ0QiUAj4hIHvGBvp/CC3PAT8ZlhYicJCKPp6wNiA8dv4J+/zSkrwHwZrpHy2rvewDeLOIj/InIk0P+jxGeSUSeCE+hdRNzjdkdCPM6fC1/FV5gtPATeE3dVQD+Gv7r/Hh4TcVm+K/rNQBeHubQK+G1TzfBj/918BqfK6FahxyAaefcF+EphscFzdoA/Ibp7fCalu8s8PmqoXzrQPgpC7jmegCvEH9eZy38y3DzAq67En7z9icIX23w560uEJHDQhuGReS4lOsjhM3IQyLy4nBtj4j0dRT7HoA3hLUAEXlCOK9wHLwm5VMAPg3aFATcCWC9hLNvAP4MXquA8Ps0+PNaref4GYDfFz0rVxKRJ8z3DB3r5Y/gz9p0limF+QERKcFrv5ZqXTernfAfY0V4+qc117kfr4fKi2PhN/KnhrnMY8b9eWS4Vy30/fUIGz4ROQ/AXvi1UwVQFn9e7HwoCvBagY/Cr4Uzg2aOP7LSMNeaXercTcNNAJ4e3jnL0b4VjXnkHODnyfUpl79IRIpBU3wefN8NAXjUOVcTkWcgyDkRORIq5y4GcFqQc0POuW/Dy7knLaLpNyK8+xYh57qOrm+KnHNl+LMov4B/2S7VOmAhdf0Ufqe6Ff6szlZ49d5c+C8ATxJ/eLPTVPNSAK8LauvjMc/Xu/OHmS8A8I9B9fhLeHUz4A903wQ/EbgP3gLg7aF8N1S5/QA+1zoIB6+m30T/Xx3y3wo9sPcWAKeHg3Pb4c8iAMD74IX0FvEHN98X8j8BoF9E7gDwv+FVpN3EFQD+exizzoPW34HXtLSwHV7bczT8V+8QgD+Hpwtr8JvE7wJ4Y9Co1eHnztHwVNjh8OdQdsF/tW4A8Ax4imFfqKMIYHOgPjbAfzUNwJ9TOhH+PNsjzrlfzfdg4WWVCent8FrF2zH/PL4SXv18G/ycfpdz7pH56nPOjcJvJI9zzm0OedvhNRXXhLnxffiN4ULxZwDeEq79CXwfMj4NPy63BGr6k/Aby/MA3CYiv4TfpP9zR1vL8GP3tUBDN+EtXRE0BlfDv8SvDnl74M+ifTm05afwFOF8eJMEVwfw5+UuBPxLQUS+HcqsA3BDkA2bAXzLOZf2lTwn5mjni+E/Ku8UkXF4urBlyXsZ/Dw5HV6+vhqe0v16aNNXMLs/3w2//qvw9O0R8C+iE+HPOV7onLst1PkuePrlRmpqKbThW/Bao1PC/3dg/vm5BUBD/CHdzoPBS5q7aQgavA/Aj8tytO83AZfAn/9p4c3w1OkW+PX41pTrtsDTZj8D8L4g574EL/+3ws+rltw6BSrn/h5eNg0AuDrUcwP8elkoLgOwdpFyruv4rQvzISL9zrnJ8LV1A/xCXxFc5UqDiOyAP9i291C3ZTkh3gLuXc65WV/4C7x+E/xh1I8uU3s2wh9Qf/0Cyr4E/jzP+51z5bDpuxbASW4Bri0MhoMBkrM5+E3N5a1zZCsBK719KwHLLeeWUH8W3phjRcm5Fc3tLRGfEZGT4L/eL7cN0f+XuAj+K3hJm6LlhnPuBvgN+kKQg9d+3BC0RgLgbw61oDAYOrBJRJ4NL2evgfeptJKw0ttn8HTwD1eanPut0xQZDAaDwWAwLAUrwSTfYDAYDAaD4ZDDNkUGg8FgMBgMsE2RwWAwGAwGAwDbFBkMBoPBYDAAsE2RwWAwGAwGAwDbFBkMBoPBYDAAAP4fn0nXNL9Eou4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_X[i], cmap=plt.cm.binary)\n",
    "    # The CIFAR labels happen to be arrays, \n",
    "    # which is why you need the extra index\n",
    "    plt.xlabel(classNames[train_Y[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Chuẩn hoá dữ liệu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Đưa dữ liệu về trong khoảng (0,1). Tại sao phải làm như thế này thì tôi đã giải thích tại bài trước. [Xem ở đây](../setup-and-first-code.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X = train_X.astype(\"float\") / 255.0\n",
    "test_X = test_X.astype(\"float\") / 255.0\n",
    "valid_X = valid_X.astype(\"float\") / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "lb = LabelBinarizer()\n",
    "train_Y = train_Y.reshape((train_Y.shape[0], 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nhãn được đánh từ 0 đến 42 tương đương với danh sách như bên trên."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chuyển về dạng one hot.\n",
    "\n",
    "One hot chỉ là cách chuyển nhãn về sao cho đảm bảo tính tương đồng của các nhãn với nhau. Hình ảnh dưới đây minh hoạ ma trận onehot.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../img/onehot.jpeg\" width=600/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_Y = lb.fit_transform(train_Y)\n",
    "valid_Y = lb.fit_transform(valid_Y)\n",
    "test_Y = lb.fit_transform(test_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 1, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Thiết kế model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./img/arch.png\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chúng ta thực hiện các phép tích chập, phép max-pooling và sau đó đưa vào mạng nửa kết nối đầy đủ (half fully-connected network) và giảm chiều lớp xuống đúng bằng số lượng class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = 1e-2\n",
    "batch_size = 64\n",
    "num_epochs = 30 # Accuracy: 74% within 30 epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "width = 32\n",
    "height = 32\n",
    "classes = 43"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "shape = (width,height, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Conv2D(32, (3 , 3), padding='same', input_shape=shape))\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Conv2D(32, (3 , 3), padding='same'))\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Conv2D(64, (3 , 3), padding='same'))\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Conv2D(64, (3 , 3), padding='same'))\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Đưa vào mạng Neuron không kết nối đầy đủ vì có sử dụng Dropout."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Flatten())\n",
    "model.add(Dense(512))\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Activation(\"softmax\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kiểm tra lại cấu trúc của mô hình trước khi train."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_8 (Conv2D)            (None, 32, 32, 32)        896       \n",
      "_________________________________________________________________\n",
      "activation_12 (Activation)   (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_10 (Batc (None, 32, 32, 32)        128       \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 32, 32, 32)        9248      \n",
      "_________________________________________________________________\n",
      "activation_13 (Activation)   (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_11 (Batc (None, 32, 32, 32)        128       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 16, 16, 64)        18496     \n",
      "_________________________________________________________________\n",
      "activation_14 (Activation)   (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_12 (Batc (None, 16, 16, 64)        256       \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 16, 16, 64)        36928     \n",
      "_________________________________________________________________\n",
      "activation_15 (Activation)   (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_13 (Batc (None, 16, 16, 64)        256       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 512)               2097664   \n",
      "_________________________________________________________________\n",
      "activation_16 (Activation)   (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_14 (Batc (None, 512)               2048      \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 43)                22059     \n",
      "_________________________________________________________________\n",
      "activation_17 (Activation)   (None, 43)                0         \n",
      "=================================================================\n",
      "Total params: 2,188,107\n",
      "Trainable params: 2,186,699\n",
      "Non-trainable params: 1,408\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Tăng cường data khi train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ta có thể xoay, zoom to, thay đổi kích cỡ để có nhiều đặc trưng hơn mà vẫn giữ nguyên nhãn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "aug = ImageDataGenerator(rotation_range=18, zoom_range=0.15, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.15, horizontal_flip=True, fill_mode=\"nearest\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chỉ số decay nhằm điều chỉnh learning rate. Đọc thêm ở đây https://towardsdatascience.com/learning-rate-schedules-and-adaptive-learning-rate-methods-for-deep-learning-2c8f433990d1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = SGD(lr=learning_rate, momentum=0.9, decay=learning_rate / num_epochs)\n",
    "model.compile(loss=\"categorical_crossentropy\", optimizer=opt, metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print(\"Start Training...\")\n",
    "H = model.fit_generator(aug.flow(train_X, train_Y, batch_size=batch_size), validation_data=(valid_X, valid_Y), \n",
    "                        steps_per_epoch=train_X.shape[0] // batch_size, epochs=num_epochs, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6. Lưu model và load lại nhằm inference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lưu lại model sau khi train xong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.save('ngocnet.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trong notebook này, tôi đã train trên Google Colab và sử dụng lại pretrained-model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.load_model(\"ngocnet.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = model.predict(np.array(test_X[0:1]/255.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x142420110>"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(test_X[50], cmap=plt.cm.binary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_label = np.argmax(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Dangerous curve to the left'"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classNames[predicted_label]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Như bạn đã thấy với một ảnh ngãu nhiên ta đã dự đoán đúng ý nghĩa của biển báo.\n",
    "\n",
    "Vậy là tôi đã chia sẻ xong cách xây dựng một deep learning model để nhận biết biển báo giao thông. Chia sẻ bài viết này nếu bạn thấy hữu ích nhé.\n",
    "\n",
    "Thân."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
